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2405.10930
|
Submodular Information Selection for Hypothesis Testing with
Misclassification Penalties
|
We consider the problem of selecting an optimal subset of information sources for a hypothesis testing/classification task where the goal is to identify the true state of the world from a finite set of hypotheses, based on finite observation samples from the sources. In order to characterize the learning performance, we propose a misclassification penalty framework, which enables nonuniform treatment of different misclassification errors. In a centralized Bayesian learning setting, we study two variants of the subset selection problem: (i) selecting a minimum cost information set to ensure that the maximum penalty of misclassifying the true hypothesis is below a desired bound and (ii) selecting an optimal information set under a limited budget to minimize the maximum penalty of misclassifying the true hypothesis. Under certain assumptions, we prove that the objective (or constraints) of these combinatorial optimization problems are weak (or approximate) submodular, and establish high-probability performance guarantees for greedy algorithms. Further, we propose an alternate metric for information set selection which is based on the total penalty of misclassification. We prove that this metric is submodular and establish near-optimal guarantees for the greedy algorithms for both the information set selection problems. Finally, we present numerical simulations to validate our theoretical results over several randomly generated instances.
|
http://arxiv.org/pdf/2405.10930v3
|
[
"Jayanth Bhargav",
"Mahsa Ghasemi",
"Shreyas Sundaram"
] |
2024-06-28T03:51:23Z
|
2024-05-17T17:31:02Z
|
2406.19636
|
Enforcing Equity in Neural Climate Emulators
|
Neural network emulators have become an invaluable tool for a wide variety of climate and weather prediction tasks. While showing incredibly promising results, these networks do not have an inherent ability to produce equitable predictions. That is, they are not guaranteed to provide a uniform quality of prediction along any particular class or group of people. This potential for inequitable predictions motivates the need for explicit representations of fairness in these neural networks. To that end, we draw on methods for enforcing analytical physical constraints in neural networks to bias networks towards more equitable predictions. We demonstrate the promise of this methodology using the task of climate model emulation. Specifically, we propose a custom loss function which punishes emulators with unequal quality of predictions across any prespecified regions or category, here defined using human development index (HDI). This loss function weighs a standard loss metric such as mean squared error against another metric which captures inequity along the equity category (HDI), allowing us to adjust the priority of each term before training. Importantly, the loss function does not specify a particular definition of equity to bias the neural network towards, opening the door for custom fairness metrics. Our results show that neural climate emulators trained with our loss function provide more equitable predictions and that the equity metric improves with greater weighting in the loss function. We empirically demonstrate that while there is a tradeoff between accuracy and equity when prioritizing the latter during training, an appropriate selection of the equity priority hyperparameter can minimize loss of performance.
|
http://arxiv.org/pdf/2406.19636v1
|
[
"William Yik",
"Sam J. Silva"
] |
2024-06-28T03:47:54Z
|
2024-06-28T03:47:54Z
|
2406.19635
|
Model Predictive Simulation Using Structured Graphical Models and
Transformers
|
We propose an approach to simulating trajectories of multiple interacting agents (road users) based on transformers and probabilistic graphical models (PGMs), and apply it to the Waymo SimAgents challenge. The transformer baseline is based on the MTR model, which predicts multiple future trajectories conditioned on the past trajectories and static road layout features. We then improve upon these generated trajectories using a PGM, which contains factors which encode prior knowledge, such as a preference for smooth trajectories, and avoidance of collisions with static obstacles and other moving agents. We perform (approximate) MAP inference in this PGM using the Gauss-Newton method. Finally we sample $K=32$ trajectories for each of the $N sim 100$ agents for the next $T=8 Delta$ time steps, where $Delta=10$ is the sampling rate per second. Following the Model Predictive Control (MPC) paradigm, we only return the first element of our forecasted trajectories at each step, and then we replan, so that the simulation can constantly adapt to its changing environment. We therefore call our approach "Model Predictive Simulation" or MPS. We show that MPS improves upon the MTR baseline, especially in safety critical metrics such as collision rate. Furthermore, our approach is compatible with any underlying forecasting model, and does not require extra training, so we believe it is a valuable contribution to the community.
|
http://arxiv.org/pdf/2406.19635v1
|
[
"Xinghua Lou",
"Meet Dave",
"Shrinu Kushagra",
"Miguel Lazaro-Gredilla",
"Kevin Murphy"
] |
2024-06-28T03:46:53Z
|
2024-06-28T03:46:53Z
|
2406.19631
|
Personalized Interpretation on Federated Learning: A Virtual Concepts
approach
|
Tackling non-IID data is an open challenge in federated learning research. Existing FL methods, including robust FL and personalized FL, are designed to improve model performance without consideration of interpreting non-IID across clients. This paper aims to design a novel FL method to robust and interpret the non-IID data across clients. Specifically, we interpret each client's dataset as a mixture of conceptual vectors that each one represents an interpretable concept to end-users. These conceptual vectors could be pre-defined or refined in a human-in-the-loop process or be learnt via the optimization procedure of the federated learning system. In addition to the interpretability, the clarity of client-specific personalization could also be applied to enhance the robustness of the training process on FL system. The effectiveness of the proposed method have been validated on benchmark datasets.
|
http://arxiv.org/pdf/2406.19631v1
|
[
"Peng Yan",
"Guodong Long",
"Jing Jiang",
"Michael Blumenstein"
] |
2024-06-28T03:39:45Z
|
2024-06-28T03:39:45Z
|
2305.13650
|
Robust Model-Based Optimization for Challenging Fitness Landscapes
|
Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training set (protein sequences and fitness) and proposes candidates to explore next. These methods are challenged by sparsity of high-fitness samples in the training set, a problem that has been in the literature. A less recognized but equally important problem stems from the distribution of training samples in the design space: leading methods are not designed for scenarios where the desired optimum is in a region that is not only poorly represented in training data, but also relatively far from the highly represented low-fitness regions. We show that this problem of "separation" in the design space is a significant bottleneck in existing model-based optimization tools and propose a new approach that uses a novel VAE as its search model to overcome the problem. We demonstrate its advantage over prior methods in robustly finding improved samples, regardless of the imbalance and separation between low- and high-fitness samples. Our comprehensive benchmark on real and semi-synthetic protein datasets as well as solution design for physics-informed neural networks, showcases the generality of our approach in discrete and continuous design spaces. Our implementation is available at https://github.com/sabagh1994/PGVAE.
|
http://arxiv.org/pdf/2305.13650v3
|
[
"Saba Ghaffari",
"Ehsan Saleh",
"Alexander G. Schwing",
"Yu-Xiong Wang",
"Martin D. Burke",
"Saurabh Sinha"
] |
2024-06-28T03:33:28Z
|
2023-05-23T03:47:32Z
|
2407.01615
|
Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving
Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints
|
In response to carbon-neutral policies in developed countries, electric vehicles route optimization has gained importance for logistics companies. With the increasing focus on customer expectations and the shift towards more customer-oriented business models, the integration of delivery time-windows has become essential in logistics operations. Recognizing the critical nature of these developments, this article studies the heterogeneous electric vehicle routing problem with time-window constraints (HEVRPTW). To solve this variant of vehicle routing problem (VRP), we propose a DRL-based approach, named Edge-enhanced Dual attentIon encoderR and feature-EnhanCed dual aTtention decoder (Edge-DIRECT). Edge-DIRECT features an extra graph representation, the node connectivity of which is based on the overlap of customer time-windows. Edge-DIRECT's self-attention encoding mechanism is enhanced by exploiting the energy consumption and travel time between the locations. To effectively account for the heterogeneity of the EVs' fleet, a dual attention decoder has been introduced. Experimental results based on two real-world datasets reveal that Edge-DIRECT outperforms a state-of-the-art DRL-based method and a well-established heuristic approach in solution quality and execution time. Furthermore, it exhibits competitive performance when compared to another leading heuristic method.
|
http://arxiv.org/pdf/2407.01615v1
|
[
"Arash Mozhdehi",
"Mahdi Mohammadizadeh",
"Xin Wang"
] |
2024-06-28T03:18:12Z
|
2024-06-28T03:18:12Z
|
2403.05743
|
Forecasting Electricity Market Signals via Generative AI
|
This paper presents a generative artificial intelligence approach to probabilistic forecasting of electricity market signals, such as real-time locational marginal prices and area control error signals. Inspired by the Wiener-Kallianpur innovation representation of nonparametric time series, we propose a weak innovation autoencoder architecture and a novel deep learning algorithm that extracts the canonical independent and identically distributed innovation sequence of the time series, from which samples of future time series are generated. The validity of the proposed approach is established by proving that, under ideal training conditions, the generated samples have the same conditional probability distribution as that of the ground truth. Three applications involving highly dynamic and volatile time series in real-time market operations are considered: (i) locational marginal price forecasting for self-scheduled resources such as battery storage participants, (ii) interregional price spread forecasting for virtual bidders in interchange markets, and (iii) area control error forecasting for frequency regulations. Numerical studies based on market data from multiple independent system operators demonstrate the superior performance of the proposed generative forecaster over leading classical and modern machine learning techniques under both probabilistic and point forecasting metrics.
|
http://arxiv.org/pdf/2403.05743v4
|
[
"Xinyi Wang",
"Qing Zhao",
"Lang Tong"
] |
2024-06-28T03:17:12Z
|
2024-03-09T00:41:30Z
|
2406.19622
|
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve
Adversarial Robustness
|
The security and robustness of deep neural networks (DNNs) have become increasingly concerning. This paper aims to provide both a theoretical foundation and a practical solution to ensure the reliability of DNNs. We explore the concept of Lipschitz continuity to certify the robustness of DNNs against adversarial attacks, which aim to mislead the network with adding imperceptible perturbations into inputs. We propose a novel algorithm that remaps the input domain into a constrained range, reducing the Lipschitz constant and potentially enhancing robustness. Unlike existing adversarially trained models, where robustness is enhanced by introducing additional examples from other datasets or generative models, our method is almost cost-free as it can be integrated with existing models without requiring re-training. Experimental results demonstrate the generalizability of our method, as it can be combined with various models and achieve enhancements in robustness. Furthermore, our method achieves the best robust accuracy for CIFAR10, CIFAR100, and ImageNet datasets on the RobustBench leaderboard.
|
http://arxiv.org/pdf/2406.19622v1
|
[
"Erh-Chung Chen",
"Pin-Yu Chen",
"I-Hsin Chung",
"Che-Rung Lee"
] |
2024-06-28T03:10:36Z
|
2024-06-28T03:10:36Z
|
2406.19621
|
Machine-Learning-Driven Runtime Optimization of BLAS Level 3 on Modern
Multi-Core Systems
|
BLAS Level 3 operations are essential for scientific computing, but finding the optimal number of threads for multi-threaded implementations on modern multi-core systems is challenging. We present an extension to the Architecture and Data-Structure Aware Linear Algebra (ADSALA) library that uses machine learning to optimize the runtime of all BLAS Level 3 operations. Our method predicts the best number of threads for each operation based on the matrix dimensions and the system architecture. We test our method on two HPC platforms with Intel and AMD processors, using MKL and BLIS as baseline BLAS implementations. We achieve speedups of 1.5 to 3.0 for all operations, compared to using the maximum number of threads. We also analyze the runtime patterns of different BLAS operations and explain the sources of speedup. Our work shows the effectiveness and generality of the ADSALA approach for optimizing BLAS routines on modern multi-core systems.
|
http://arxiv.org/pdf/2406.19621v1
|
[
"Yufan Xia",
"Giuseppe Maria Junior Barca"
] |
2024-06-28T03:07:53Z
|
2024-06-28T03:07:53Z
|
2407.00127
|
Multi-Species Object Detection in Drone Imagery for Population
Monitoring of Endangered Animals
|
Animal populations worldwide are rapidly declining, and a technology that can accurately count endangered species could be vital for monitoring population changes over several years. This research focused on fine-tuning object detection models for drone images to create accurate counts of animal species. Hundreds of images taken using a drone and large, openly available drone-image datasets were used to fine-tune machine learning models with the baseline YOLOv8 architecture. We trained 30 different models, with the largest having 43.7 million parameters and 365 layers, and used hyperparameter tuning and data augmentation techniques to improve accuracy. While the state-of-the-art YOLOv8 baseline had only 0.7% accuracy on a dataset of safari animals, our models had 95% accuracy on the same dataset. Finally, we deployed the models on the Jetson Orin Nano for demonstration of low-power real-time species detection for easy inference on drones.
|
http://arxiv.org/pdf/2407.00127v1
|
[
"Sowmya Sankaran"
] |
2024-06-28T03:03:55Z
|
2024-06-28T03:03:55Z
|
2406.19619
|
ScoreFusion: fusing score-based generative models via Kullback-Leibler
barycenters
|
We study the problem of fusing pre-trained (auxiliary) generative models to enhance the training of a target generative model. We propose using KL-divergence weighted barycenters as an optimal fusion mechanism, in which the barycenter weights are optimally trained to minimize a suitable loss for the target population. While computing the optimal KL-barycenter weights can be challenging, we demonstrate that this process can be efficiently executed using diffusion score training when the auxiliary generative models are also trained based on diffusion score methods. Moreover, we show that our fusion method has a dimension-free sample complexity in total variation distance provided that the auxiliary models are well fitted for their own task and the auxiliary tasks combined capture the target well. The main takeaway of our method is that if the auxiliary models are well-trained and can borrow features from each other that are present in the target, our fusion method significantly improves the training of generative models. We provide a concise computational implementation of the fusion algorithm, and validate its efficiency in the low-data regime with numerical experiments involving mixtures models and image datasets.
|
http://arxiv.org/pdf/2406.19619v1
|
[
"Hao Liu",
"Junze",
"Ye",
"Jose Blanchet",
"Nian Si"
] |
2024-06-28T03:02:25Z
|
2024-06-28T03:02:25Z
|
2406.19617
|
Stochastic Zeroth-Order Optimization under Strongly Convexity and
Lipschitz Hessian: Minimax Sample Complexity
|
Optimization of convex functions under stochastic zeroth-order feedback has been a major and challenging question in online learning. In this work, we consider the problem of optimizing second-order smooth and strongly convex functions where the algorithm is only accessible to noisy evaluations of the objective function it queries. We provide the first tight characterization for the rate of the minimax simple regret by developing matching upper and lower bounds. We propose an algorithm that features a combination of a bootstrapping stage and a mirror-descent stage. Our main technical innovation consists of a sharp characterization for the spherical-sampling gradient estimator under higher-order smoothness conditions, which allows the algorithm to optimally balance the bias-variance tradeoff, and a new iterative method for the bootstrapping stage, which maintains the performance for unbounded Hessian.
|
http://arxiv.org/pdf/2406.19617v1
|
[
"Qian Yu",
"Yining Wang",
"Baihe Huang",
"Qi Lei",
"Jason D. Lee"
] |
2024-06-28T02:56:22Z
|
2024-06-28T02:56:22Z
|
2309.17366
|
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property
Prediction with 3D Information
|
Molecular property prediction, crucial for early drug candidate screening and optimization, has seen advancements with deep learning-based methods. While deep learning-based methods have advanced considerably, they often fall short in fully leveraging 3D spatial information. Specifically, current molecular encoding techniques tend to inadequately extract spatial information, leading to ambiguous representations where a single one might represent multiple distinct molecules. Moreover, existing molecular modeling methods focus predominantly on the most stable 3D conformations, neglecting other viable conformations present in reality. To address these issues, we propose 3D-Mol, a novel approach designed for more accurate spatial structure representation. It deconstructs molecules into three hierarchical graphs to better extract geometric information. Additionally, 3D-Mol leverages contrastive learning for pretraining on 20 million unlabeled data, treating their conformations with identical topological structures as weighted positive pairs and contrasting ones as negatives, based on the similarity of their 3D conformation descriptors and fingerprints. We compare 3D-Mol with various state-of-the-art baselines on 7 benchmarks and demonstrate our outstanding performance.
|
http://arxiv.org/pdf/2309.17366v3
|
[
"Taojie Kuang",
"Yiming Ren",
"Zhixiang Ren"
] |
2024-06-28T02:56:10Z
|
2023-09-28T10:05:37Z
|
2406.19615
|
VarteX: Enhancing Weather Forecast through Distributed Variable
Representation
|
Weather forecasting is essential for various human activities. Recent data-driven models have outperformed numerical weather prediction by utilizing deep learning in forecasting performance. However, challenges remain in efficiently handling multiple meteorological variables. This study proposes a new variable aggregation scheme and an efficient learning framework for that challenge. Experiments show that VarteX outperforms the conventional model in forecast performance, requiring significantly fewer parameters and resources. The effectiveness of learning through multiple aggregations and regional split training is demonstrated, enabling more efficient and accurate deep learning-based weather forecasting.
|
http://arxiv.org/pdf/2406.19615v1
|
[
"Ayumu Ueyama",
"Kazuhiko Kawamoto",
"Hiroshi Kera"
] |
2024-06-28T02:42:30Z
|
2024-06-28T02:42:30Z
|
2406.19614
|
A Survey on Data Quality Dimensions and Tools for Machine Learning
|
Machine learning (ML) technologies have become substantial in practically all aspects of our society, and data quality (DQ) is critical for the performance, fairness, robustness, safety, and scalability of ML models. With the large and complex data in data-centric AI, traditional methods like exploratory data analysis (EDA) and cross-validation (CV) face challenges, highlighting the importance of mastering DQ tools. In this survey, we review 17 DQ evaluation and improvement tools in the last 5 years. By introducing the DQ dimensions, metrics, and main functions embedded in these tools, we compare their strengths and limitations and propose a roadmap for developing open-source DQ tools for ML. Based on the discussions on the challenges and emerging trends, we further highlight the potential applications of large language models (LLMs) and generative AI in DQ evaluation and improvement for ML. We believe this comprehensive survey can enhance understanding of DQ in ML and could drive progress in data-centric AI. A complete list of the literature investigated in this survey is available on GitHub at: https://github.com/haihua0913/awesome-dq4ml.
|
http://arxiv.org/pdf/2406.19614v1
|
[
"Yuhan Zhou",
"Fengjiao Tu",
"Kewei Sha",
"Junhua Ding",
"Haihua Chen"
] |
2024-06-28T02:41:33Z
|
2024-06-28T02:41:33Z
|
2406.18820
|
Universal Checkpointing: Efficient and Flexible Checkpointing for Large
Scale Distributed Training
|
Existing checkpointing approaches seem ill-suited for distributed training even though hardware limitations make model parallelism, i.e., sharding model state across multiple accelerators, a requirement for model scaling. Consolidating distributed model state into a single checkpoint unacceptably slows down training, and is impractical at extreme scales. Distributed checkpoints, in contrast, are tightly coupled to the model parallelism and hardware configurations of the training run, and thus unusable on different configurations. To address this problem, we propose Universal Checkpointing, a technique that enables efficient checkpoint creation while providing the flexibility of resuming on arbitrary parallelism strategy and hardware configurations. Universal Checkpointing unlocks unprecedented capabilities for large-scale training such as improved resilience to hardware failures through continued training on remaining healthy hardware, and reduced training time through opportunistic exploitation of elastic capacity. The key insight of Universal Checkpointing is the selection of the optimal representation in each phase of the checkpointing life cycle: distributed representation for saving, and consolidated representation for loading. This is achieved using two key mechanisms. First, the universal checkpoint format, which consists of a consolidated representation of each model parameter and metadata for mapping parameter fragments into training ranks of arbitrary model-parallelism configuration. Second, the universal checkpoint language, a simple but powerful specification language for converting distributed checkpoints into the universal checkpoint format. Our evaluation demonstrates the effectiveness and generality of Universal Checkpointing on state-of-the-art model architectures and a wide range of parallelism techniques.
|
http://arxiv.org/pdf/2406.18820v2
|
[
"Xinyu Lian",
"Sam Ade Jacobs",
"Lev Kurilenko",
"Masahiro Tanaka",
"Stas Bekman",
"Olatunji Ruwase",
"Minjia Zhang"
] |
2024-06-28T02:33:11Z
|
2024-06-27T01:28:30Z
|
2402.07249
|
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular
Property Prediction: A Systematic Survey
|
The precise prediction of molecular properties is essential for advancements in drug development, particularly in virtual screening and compound optimization. The recent introduction of numerous deep learning-based methods has shown remarkable potential in enhancing molecular property prediction (MPP), especially improving accuracy and insights into molecular structures. Yet, two critical questions arise: does the integration of domain knowledge augment the accuracy of molecular property prediction and does employing multi-modal data fusion yield more precise results than unique data source methods? To explore these matters, we comprehensively review and quantitatively analyze recent deep learning methods based on various benchmarks. We discover that integrating molecular information significantly improves molecular property prediction (MPP) for both regression and classification tasks. Specifically, regression improvements, measured by reductions in root mean square error (RMSE), are up to 4.0%, while classification enhancements, measured by the area under the receiver operating characteristic curve (ROC-AUC), are up to 1.7%. We also discover that enriching 2D graphs with 1D SMILES boosts multi-modal learning performance for regression tasks by up to 9.1%, and augmenting 2D graphs with 3D information increases performance for classification tasks by up to 13.2%, with both enhancements measured using ROC-AUC. The two consolidated insights offer crucial guidance for future advancements in drug discovery.
|
http://arxiv.org/pdf/2402.07249v3
|
[
"Taojie Kuang",
"Pengfei Liu",
"Zhixiang Ren"
] |
2024-06-28T02:32:03Z
|
2024-02-11T17:29:58Z
|
2403.06873
|
Last Iterate Convergence of Incremental Methods and Applications in
Continual Learning
|
Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees have primarily been established for the ergodic (average) iterate. Motivated by applications in continual learning, we obtain the first convergence guarantees for the last iterate of both incremental gradient and incremental proximal methods, in general convex smooth (for both) and convex Lipschitz (for the proximal variants) settings. Our oracle complexity bounds for the last iterate nearly match (i.e., match up to a square-root-log or a log factor) the best known oracle complexity bounds for the average iterate, for both classes of methods. We further obtain generalizations of our results to weighted averaging of the iterates with increasing weights and for randomly permuted ordering of updates. We study incremental proximal methods as a model of continual learning with generalization and argue that large amount of regularization is crucial to preventing catastrophic forgetting. Our results generalize last iterate guarantees for incremental methods compared to state of the art, as such results were previously known only for overparameterized linear models, which correspond to convex quadratic problems with infinitely many solutions.
|
http://arxiv.org/pdf/2403.06873v2
|
[
"Xufeng Cai",
"Jelena Diakonikolas"
] |
2024-06-28T02:25:20Z
|
2024-03-11T16:24:26Z
|
2407.01456
|
Information-Theoretic Foundations for Neural Scaling Laws
|
Neural scaling laws aim to characterize how out-of-sample error behaves as a function of model and training dataset size. Such scaling laws guide allocation of a computational resources between model and data processing to minimize error. However, existing theoretical support for neural scaling laws lacks rigor and clarity, entangling the roles of information and optimization. In this work, we develop rigorous information-theoretic foundations for neural scaling laws. This allows us to characterize scaling laws for data generated by a two-layer neural network of infinite width. We observe that the optimal relation between data and model size is linear, up to logarithmic factors, corroborating large-scale empirical investigations. Concise yet general results of the kind we establish may bring clarity to this topic and inform future investigations.
|
http://arxiv.org/pdf/2407.01456v1
|
[
"Hong Jun Jeon",
"Benjamin Van Roy"
] |
2024-06-28T02:20:54Z
|
2024-06-28T02:20:54Z
|
2407.01614
|
Enhancing Stability for Large Models Training in Constrained Bandwidth
Networks
|
Training extremely large language models with billions of parameters is a computationally intensive task that pushes the limits of current data parallel training systems. While techniques like ZeRO++ have enabled efficient distributed training of such giant models on inexpensive low-bandwidth clusters, they can suffer from convergence issues due to potential race conditions in the hierarchical partitioning (hpZ) scheme employed to reduce cross-machine communication. In this work, we first show how these race conditions cause instability when training models with billions of parameters. We then propose a modification to the partitioning algorithm that addresses these convergence challenges while maintaining competitive training efficiency. Empirical evaluation on training the multi-billion parameters Falcon Models and Llama-2 models demonstrates the updated algorithm's ability to achieve reliable convergence on these massive models, where stock ZeRO++ hpZ fails to converge. The updated algorithm enables robust training of larger models with 98% throughput and model training speed improvement without sacrificing the quality of convergence.
|
http://arxiv.org/pdf/2407.01614v1
|
[
"Yun Dai",
"Tejas Dharamsi",
"Byron Hsu",
"Tao Song",
"Hamed Firooz"
] |
2024-06-28T01:46:10Z
|
2024-06-28T01:46:10Z
|
2406.19596
|
Optimizing Cyber Defense in Dynamic Active Directories through
Reinforcement Learning
|
This paper addresses a significant gap in Autonomous Cyber Operations (ACO) literature: the absence of effective edge-blocking ACO strategies in dynamic, real-world networks. It specifically targets the cybersecurity vulnerabilities of organizational Active Directory (AD) systems. Unlike the existing literature on edge-blocking defenses which considers AD systems as static entities, our study counters this by recognizing their dynamic nature and developing advanced edge-blocking defenses through a Stackelberg game model between attacker and defender. We devise a Reinforcement Learning (RL)-based attack strategy and an RL-assisted Evolutionary Diversity Optimization-based defense strategy, where the attacker and defender improve each other strategy via parallel gameplay. To address the computational challenges of training attacker-defender strategies on numerous dynamic AD graphs, we propose an RL Training Facilitator that prunes environments and neural networks to eliminate irrelevant elements, enabling efficient and scalable training for large graphs. We extensively train the attacker strategy, as a sophisticated attacker model is essential for a robust defense. Our empirical results successfully demonstrate that our proposed approach enhances defender's proficiency in hardening dynamic AD graphs while ensuring scalability for large-scale AD.
|
http://arxiv.org/pdf/2406.19596v1
|
[
"Diksha Goel",
"Kristen Moore",
"Mingyu Guo",
"Derui Wang",
"Minjune Kim",
"Seyit Camtepe"
] |
2024-06-28T01:37:46Z
|
2024-06-28T01:37:46Z
|
2404.14527
|
Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU
Heterogeneity
|
Large language models (LLMs) are increasingly integrated into many online services, yet they remain cost-prohibitive to deploy due to the requirement of expensive GPU instances. Prior work has addressed the high cost of LLM serving by improving the inference engine, but less attention has been given to selecting the most cost-efficient GPU type(s) for a specific LLM service. There is a large and growing landscape of GPU types and, within these options, higher cost does not always lead to increased performance. Instead, through a comprehensive investigation, we find that three key LLM service characteristics (request size, request rate, SLO) strongly influence GPU cost efficiency, and differing GPU types are most cost efficient for differing LLM service settings. As a result, the most cost-efficient allocation for a given service is typically a mix of heterogeneous GPU types. Based on this analysis, we introduce M'elange, a GPU allocation framework that navigates these diverse LLM service characteristics and heterogeneous GPU option space to automatically and efficiently derive the minimal-cost GPU allocation for a given LLM service. We formulate the GPU allocation task as a cost-aware bin packing problem where GPUs are bins and items are slices of the service workload. Our formulation's constraints account for a service's unique characteristics, allowing M'elange to be flexible to support diverse service settings and heterogeneity-aware to adapt the GPU allocation to a specific service. Compared to using only a single GPU type, M'elange reduces deployment costs by up to 77% in conversational settings, 33% in document-based settings, and 51% in a mixed setting.
|
http://arxiv.org/pdf/2404.14527v3
|
[
"Tyler Griggs",
"Xiaoxuan Liu",
"Jiaxiang Yu",
"Doyoung Kim",
"Wei-Lin Chiang",
"Alvin Cheung",
"Ion Stoica"
] |
2024-06-28T01:24:22Z
|
2024-04-22T18:56:18Z
|
2407.01613
|
Self-adaptive weights based on balanced residual decay rate for
physics-informed neural networks and deep operator networks
|
Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work, we demonstrate that the failure of plain physics-informed neural networks arises from the significant discrepancy in the convergence speed of residuals at different training points, where the slowest convergence speed dominates the overall solution convergence. Based on these observations, we propose a point-wise adaptive weighting method that balances the residual decay rate across different training points. The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics-informed neural networks and physics-informed deep operator networks. Through extensive numerical results we demonstrate that our proposed approach of balanced residual decay rates offers several advantages, including bounded weights, high prediction accuracy, fast convergence speed, low training uncertainty, low computational cost and ease of hyperparameter tuning.
|
http://arxiv.org/pdf/2407.01613v1
|
[
"Wenqian Chen",
"Amanda A. Howard",
"Panos Stinis"
] |
2024-06-28T00:53:48Z
|
2024-06-28T00:53:48Z
|
2406.19589
|
Network Bending of Diffusion Models for Audio-Visual Generation
|
In this paper we present the first steps towards the creation of a tool which enables artists to create music visualizations using pre-trained, generative, machine learning models. First, we investigate the application of network bending, the process of applying transforms within the layers of a generative network, to image generation diffusion models by utilizing a range of point-wise, tensor-wise, and morphological operators. We identify a number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools. We find that this process allows for continuous, fine-grain control of image generation which can be helpful for creative applications. Next, we generate music-reactive videos using Stable Diffusion by passing audio features as parameters to network bending operators. Finally, we comment on certain transforms which radically shift the image and the possibilities of learning more about the latent space of Stable Diffusion based on these transforms.
|
http://arxiv.org/pdf/2406.19589v1
|
[
"Luke Dzwonczyk",
"Carmine Emanuele Cella",
"David Ban"
] |
2024-06-28T00:39:17Z
|
2024-06-28T00:39:17Z
|
2406.19581
|
HarmonICA: Neural non-stationarity correction and source separation for
motor neuron interfaces
|
A major outstanding problem when interfacing with spinal motor neurons is how to accurately compensate for non-stationary effects in the signal during source separation routines, particularly when they cannot be estimated in advance. This forces current systems to instead use undifferentiated bulk signal, which limits the potential degrees of freedom for control. In this study we propose a potential solution, using an unsupervised learning algorithm to blindly correct for the effects of latent processes which drive the signal non-stationarities. We implement this methodology within the theoretical framework of a quasilinear version of independent component analysis (ICA). The proposed design, HarmonICA, sidesteps the identifiability problems of nonlinear ICA, allowing for equivalent predictability to linear ICA whilst retaining the ability to learn complex nonlinear relationships between non-stationary latents and their effects on the signal. We test HarmonICA on both invasive and non-invasive recordings both simulated and real, demonstrating an ability to blindly compensate for the non-stationary effects specific to each, and thus to significantly enhance the quality of a source separation routine.
|
http://arxiv.org/pdf/2406.19581v1
|
[
"Alexander Kenneth Clarke",
"Agnese Grison",
"Irene Mendez Guerra",
"Pranav Mamidanna",
"Shihan Ma",
"Silvia Muceli",
"Dario Farina"
] |
2024-06-28T00:08:13Z
|
2024-06-28T00:08:13Z
|
2406.19580
|
FRED: Flexible REduction-Distribution Interconnect and Communication
Implementation for Wafer-Scale Distributed Training of DNN Models
|
Distributed Deep Neural Network (DNN) training is a technique to reduce the training overhead by distributing the training tasks into multiple accelerators, according to a parallelization strategy. However, high-performance compute and interconnects are needed for maximum speed-up and linear scaling of the system. Wafer-scale systems are a promising technology that allows for tightly integrating high-end accelerators with high-speed wafer-scale interconnects, making it an attractive platform for distributed training. However, the wafer-scale interconnect should offer high performance and flexibility for various parallelization strategies to enable maximum optimizations for compute and memory usage. In this paper, we propose FRED, a wafer-scale interconnect that is tailored for the high-BW requirements of wafer-scale networks and can efficiently execute communication patterns of different parallelization strategies. Furthermore, FRED supports in-switch collective communication execution that reduces the network traffic by approximately 2X. Our results show that FRED can improve the average end-to-end training time of ResNet-152, Transformer-17B, GPT-3, and Transformer-1T by 1.76X, 1.87X, 1.34X, and 1.4X, respectively when compared to a baseline waferscale 2D-Mesh fabric.
|
http://arxiv.org/pdf/2406.19580v1
|
[
"Saeed Rashidi",
"William Won",
"Sudarshan Srinivasan",
"Puneet Gupta",
"Tushar Krishna"
] |
2024-06-28T00:05:53Z
|
2024-06-28T00:05:53Z
|
2405.05480
|
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of
Real-World SoCs
|
Floorplanning for systems-on-a-chip (SoCs) and its sub-systems is a crucial and non-trivial step of the physical design flow. It represents a difficult combinatorial optimization problem. A typical large scale SoC with 120 partitions generates a search-space of nearly 10E250. As novel machine learning (ML) approaches emerge to tackle such problems, there is a growing need for a modern benchmark that comprises a large training dataset and performance metrics that better reflect real-world constraints and objectives compared to existing benchmarks. To address this need, we present FloorSet -- two comprehensive datasets of synthetic fixed-outline floorplan layouts that reflect the distribution of real SoCs. Each dataset has 1M training samples and 100 test samples where each sample is a synthetic floor-plan. FloorSet-Prime comprises fully-abutted rectilinear partitions and near-optimal wire-length. A simplified dataset that reflects early design phases, FloorSet-Lite comprises rectangular partitions, with under 5 percent white-space and near-optimal wire-length. Both datasets define hard constraints seen in modern design flows such as shape constraints, edge-affinity, grouping constraints, and pre-placement constraints. FloorSet is intended to spur fundamental research on large-scale constrained optimization problems. Crucially, FloorSet alleviates the core issue of reproducibility in modern ML driven solutions to such problems. FloorSet is available as an open-source repository for the research community.
|
http://arxiv.org/pdf/2405.05480v2
|
[
"Uday Mallappa",
"Hesham Mostafa",
"Mikhail Galkin",
"Mariano Phielipp",
"Somdeb Majumdar"
] |
2024-06-28T00:05:14Z
|
2024-05-09T00:37:56Z
|
2406.19579
|
Private Zeroth-Order Nonsmooth Nonconvex Optimization
|
We introduce a new zeroth-order algorithm for private stochastic optimization on nonconvex and nonsmooth objectives. Given a dataset of size $M$, our algorithm ensures $(alpha,alpharho^2/2)$-R'enyi differential privacy and finds a $(delta,epsilon)$-stationary point so long as $M=tildeOmegaleft(frac{d}{deltaepsilon^3} + frac{d^{3/2}}{rhodeltaepsilon^2}right)$. This matches the optimal complexity of its non-private zeroth-order analog. Notably, although the objective is not smooth, we have privacy ``for free'' whenever $rho ge sqrt{d}epsilon$.
|
http://arxiv.org/pdf/2406.19579v1
|
[
"Qinzi Zhang",
"Hoang Tran",
"Ashok Cutkosky"
] |
2024-06-27T23:57:01Z
|
2024-06-27T23:57:01Z
|
2406.19578
|
PathAlign: A vision-language model for whole slide images in
histopathology
|
Microscopic interpretation of histopathology images underlies many important diagnostic and treatment decisions. While advances in vision-language modeling raise new opportunities for analysis of such images, the gigapixel-scale size of whole slide images (WSIs) introduces unique challenges. Additionally, pathology reports simultaneously highlight key findings from small regions while also aggregating interpretation across multiple slides, often making it difficult to create robust image-text pairs. As such, pathology reports remain a largely untapped source of supervision in computational pathology, with most efforts relying on region-of-interest annotations or self-supervision at the patch-level. In this work, we develop a vision-language model based on the BLIP-2 framework using WSIs paired with curated text from pathology reports. This enables applications utilizing a shared image-text embedding space, such as text or image retrieval for finding cases of interest, as well as integration of the WSI encoder with a frozen large language model (LLM) for WSI-based generative text capabilities such as report generation or AI-in-the-loop interactions. We utilize a de-identified dataset of over 350,000 WSIs and diagnostic text pairs, spanning a wide range of diagnoses, procedure types, and tissue types. We present pathologist evaluation of text generation and text retrieval using WSI embeddings, as well as results for WSI classification and workflow prioritization (slide-level triaging). Model-generated text for WSIs was rated by pathologists as accurate, without clinically significant error or omission, for 78% of WSIs on average. This work demonstrates exciting potential capabilities for language-aligned WSI embeddings.
|
http://arxiv.org/pdf/2406.19578v1
|
[
"Faruk Ahmed",
"Andrew Sellergren",
"Lin Yang",
"Shawn Xu",
"Boris Babenko",
"Abbi Ward",
"Niels Olson",
"Arash Mohtashamian",
"Yossi Matias",
"Greg S. Corrado",
"Quang Duong",
"Dale R. Webster",
"Shravya Shetty",
"Daniel Golden",
"Yun Liu",
"David F. Steiner",
"Ellery Wulczyn"
] |
2024-06-27T23:43:36Z
|
2024-06-27T23:43:36Z
|
2406.19573
|
On Counterfactual Interventions in Vector Autoregressive Models
|
Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we introduce the problem of counterfactual reasoning in the context of vector autoregressive (VAR) processes. We also formulate the inference of a causal model as a joint regression task where for inference we use both data with and without interventions. After learning the model, we exploit linearity of the VAR model to make exact predictions about the effects of counterfactual interventions. Furthermore, we quantify the total causal effects of past counterfactual interventions. The source code for this project is freely available at https://github.com/KurtButler/counterfactual_interventions.
|
http://arxiv.org/pdf/2406.19573v1
|
[
"Kurt Butler",
"Marija Iloska",
"Petar M. Djuric"
] |
2024-06-27T23:25:57Z
|
2024-06-27T23:25:57Z
|
2404.02319
|
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient
Compile-Time Prompt Optimization
|
In many modern LLM applications, such as retrieval augmented generation, prompts have become programs themselves. In these settings, prompt programs are repeatedly called with different user queries or data instances. A big practical challenge is optimizing such prompt programs. Recent work has mostly focused on either simple prompt programs or assumed that the general structure of a prompt program is fixed. We introduce SAMMO, a framework to perform symbolic prompt program search for compile-time optimizations of prompt programs. SAMMO represents prompt programs on a symbolic level which allows for a rich set of transformations that can be searched over during optimization. We show that SAMMO generalizes previous methods and improves the performance of complex prompts on (1) instruction tuning, (2) RAG pipeline tuning, and (3) prompt compression, across several different LLMs. We make all code available open-source at https://github.com/microsoft/sammo .
|
http://arxiv.org/pdf/2404.02319v2
|
[
"Tobias Schnabel",
"Jennifer Neville"
] |
2024-06-27T23:22:14Z
|
2024-04-02T21:35:54Z
|
2406.19566
|
Instance-Optimal Private Density Estimation in the Wasserstein Distance
|
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances. For distributions $P$ over $mathbb{R}$, we consider a strong notion of instance-optimality: an algorithm that uniformly achieves the instance-optimal estimation rate is competitive with an algorithm that is told that the distribution is either $P$ or $Q_P$ for some distribution $Q_P$ whose probability density function (pdf) is within a factor of 2 of the pdf of $P$. For distributions over $mathbb{R}^2$, we use a different notion of instance optimality. We say that an algorithm is instance-optimal if it is competitive with an algorithm that is given a constant-factor multiplicative approximation of the density of the distribution. We characterize the instance-optimal estimation rates in both these settings and show that they are uniformly achievable (up to polylogarithmic factors). Our approach for $mathbb{R}^2$ extends to arbitrary metric spaces as it goes via hierarchically separated trees. As a special case our results lead to instance-optimal private learning in TV distance for discrete distributions.
|
http://arxiv.org/pdf/2406.19566v1
|
[
"Vitaly Feldman",
"Audra McMillan",
"Satchit Sivakumar",
"Kunal Talwar"
] |
2024-06-27T22:51:06Z
|
2024-06-27T22:51:06Z
|
2309.10563
|
A Hierarchical Neural Framework for Classification and its Explanation
in Large Unstructured Legal Documents
|
Automatic legal judgment prediction and its explanation suffer from the problem of long case documents exceeding tens of thousands of words, in general, and having a non-uniform structure. Predicting judgments from such documents and extracting their explanation becomes a challenging task, more so on documents with no structural annotation. We define this problem as "scarce annotated legal documents" and explore their lack of structural information and their long lengths with a deep-learning-based classification framework which we call MESc; "Multi-stage Encoder-based Supervised with-clustering"; for judgment prediction. We explore the adaptability of LLMs with multi-billion parameters (GPT-Neo, and GPT-J) to legal texts and their intra-domain(legal) transfer learning capacity. Alongside this, we compare their performance and adaptability with MESc and the impact of combining embeddings from their last layers. For such hierarchical models, we also propose an explanation extraction algorithm named ORSE; Occlusion sensitivity-based Relevant Sentence Extractor; based on the input-occlusion sensitivity of the model, to explain the predictions with the most relevant sentences from the document. We explore these methods and test their effectiveness with extensive experiments and ablation studies on legal documents from India, the European Union, and the United States with the ILDC dataset and a subset of the LexGLUE dataset. MESc achieves a minimum total performance gain of approximately 2 points over previous state-of-the-art proposed methods, while ORSE applied on MESc achieves a total average gain of 50% over the baseline explainability scores.
|
http://arxiv.org/pdf/2309.10563v3
|
[
"Nishchal Prasad",
"Mohand Boughanem",
"Taoufik Dkaki"
] |
2024-06-27T22:40:45Z
|
2023-09-19T12:18:28Z
|
2406.19561
|
Meta-Gradient Search Control: A Method for Improving the Efficiency of
Dyna-style Planning
|
We study how a Reinforcement Learning (RL) system can remain sample-efficient when learning from an imperfect model of the environment. This is particularly challenging when the learning system is resource-constrained and in continual settings, where the environment dynamics change. To address these challenges, our paper introduces an online, meta-gradient algorithm that tunes a probability with which states are queried during Dyna-style planning. Our study compares the aggregate, empirical performance of this meta-gradient method to baselines that employ conventional sampling strategies. Results indicate that our method improves efficiency of the planning process, which, as a consequence, improves the sample-efficiency of the overall learning process. On the whole, we observe that our meta-learned solutions avoid several pathologies of conventional planning approaches, such as sampling inaccurate transitions and those that stall credit assignment. We believe these findings could prove useful, in future work, for designing model-based RL systems at scale.
|
http://arxiv.org/pdf/2406.19561v1
|
[
"Bradley Burega",
"John D. Martin",
"Luke Kapeluck",
"Michael Bowling"
] |
2024-06-27T22:24:46Z
|
2024-06-27T22:24:46Z
|
2406.19560
|
Cost-efficient Active Illumination Camera For Hyper-spectral
Reconstruction
|
Hyper-spectral imaging has recently gained increasing attention for use in different applications, including agricultural investigation, ground tracking, remote sensing and many other. However, the high cost, large physical size and complicated operation process stop hyperspectral cameras from being employed for various applications and research fields. In this paper, we introduce a cost-efficient, compact and easy to use active illumination camera that may benefit many applications. We developed a fully functional prototype of such camera. With the hope of helping with agricultural research, we tested our camera for plant root imaging. In addition, a U-Net model for spectral reconstruction was trained by using a reference hyperspectral camera's data as ground truth and our camera's data as input. We demonstrated our camera's ability to obtain additional information over a typical RGB camera. In addition, the ability to reconstruct hyperspectral data from multi-spectral input makes our device compatible to models and algorithms developed for hyperspectral applications with no modifications required.
|
http://arxiv.org/pdf/2406.19560v1
|
[
"Yuxuan Zhang",
"T. M. Sazzad",
"Yangyang Song",
"Spencer J. Chang",
"Ritesh Chowdhry",
"Tomas Mejia",
"Anna Hampton",
"Shelby Kucharski",
"Stefan Gerber",
"Barry Tillman",
"Marcio F. R. Resende",
"William M. Hammond",
"Chris H. Wilson",
"Alina Zare",
"Sanjeev J. Koppal"
] |
2024-06-27T22:19:19Z
|
2024-06-27T22:19:19Z
|
2406.19556
|
BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of
Brain Diseases
|
Recent advances have enabled the study of human brain development using brain organoids derived from stem cells. Quantifying cellular processes like mitosis in these organoids offers insights into neurodevelopmental disorders, but the manual analysis is time-consuming, and existing datasets lack specific details for brain organoid studies. We introduce BOrg, a dataset designed to study mitotic events in the embryonic development of the brain using confocal microscopy images of brain organoids. BOrg utilizes an efficient annotation pipeline with sparse point annotations and techniques that minimize expert effort, overcoming limitations of standard deep learning approaches on sparse data. We adapt and benchmark state-of-the-art object detection and cell counting models on BOrg for detecting and analyzing mitotic cells across prophase, metaphase, anaphase, and telophase stages. Our results demonstrate these adapted models significantly improve mitosis analysis efficiency and accuracy for brain organoid research compared to existing methods. BOrg facilitates the development of automated tools to quantify statistics like mitosis rates, aiding mechanistic studies of neurodevelopmental processes and disorders. Data and code are available at https://github.com/awaisrauf/borg.
|
http://arxiv.org/pdf/2406.19556v1
|
[
"Muhammad Awais",
"Mehaboobathunnisa Sahul Hameed",
"Bidisha Bhattacharya",
"Orly Reiner",
"Rao Muhammad Anwer"
] |
2024-06-27T22:16:53Z
|
2024-06-27T22:16:53Z
|
2406.19552
|
Rethinking harmless refusals when fine-tuning foundation models
|
In this paper, we investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. Through the lens of semi-realistic role-playing exercises designed to elicit such behaviors, we explore the response dynamics of LLMs post fine-tuning interventions. Our methodology involves prompting models for Chain-of-Thought (CoT) reasoning and analyzing the coherence between the reasoning traces and the resultant outputs. Notably, we identify a pervasive phenomenon we term emph{reason-based deception}, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs. We further examine the efficacy of response strategies (polite refusal versus explicit rebuttal) in curbing the occurrence of undesired behavior in subsequent outputs of multi-turn interactions. Our findings reveal that explicit rebuttals significantly outperform polite refusals in preventing the continuation of undesired outputs and nearly eliminate reason-based deception, challenging current practices in model fine-tuning. Accordingly, the two key contributions of this paper are (1) defining and studying reason-based deception, a new type of hidden behavior, and (2) demonstrating that rebuttals provide a more robust response model to harmful requests than refusals, thereby highlighting the need to reconsider the response strategies in fine-tuning approaches.
|
http://arxiv.org/pdf/2406.19552v1
|
[
"Florin Pop",
"Judd Rosenblatt",
"Diogo Schwerz de Lucena",
"Michael Vaiana"
] |
2024-06-27T22:08:22Z
|
2024-06-27T22:08:22Z
|
2406.09606
|
Cross-Modality Program Representation Learning for Electronic Design
Automation with High-Level Synthesis
|
In recent years, domain-specific accelerators (DSAs) have gained popularity for applications such as deep learning and autonomous driving. To facilitate DSA designs, programmers use high-level synthesis (HLS) to compile a high-level description written in C/C++ into a design with low-level hardware description languages that eventually synthesize DSAs on circuits. However, creating a high-quality HLS design still demands significant domain knowledge, particularly in microarchitecture decisions expressed as textit{pragmas}. Thus, it is desirable to automate such decisions with the help of machine learning for predicting the quality of HLS designs, requiring a deeper understanding of the program that consists of original code and pragmas. Naturally, these programs can be considered as sequence data. In addition, these programs can be compiled and converted into a control data flow graph (CDFG). But existing works either fail to leverage both modalities or combine the two in shallow or coarse ways. We propose ProgSG, a model that allows interaction between the source code sequence modality and the graph modality in a deep and fine-grained way. To alleviate the scarcity of labeled designs, a pre-training method is proposed based on a suite of compiler's data flow analysis tasks. Experimental results show that ProgSG reduces the RMSE of design performance predictions by up to $22%$, and identifies designs with an average of $1.10times$ and $1.26times$ (up to $8.17times$ and $13.31times$) performance improvement in design space exploration (DSE) task compared to HARP and AutoDSE, respectively.
|
http://arxiv.org/pdf/2406.09606v2
|
[
"Zongyue Qin",
"Yunsheng Bai",
"Atefeh Sohrabizadeh",
"Zijian Ding",
"Ziniu Hu",
"Yizhou Sun",
"Jason Cong"
] |
2024-06-27T22:06:19Z
|
2024-06-13T22:34:58Z
|
2404.05891
|
Condition Monitoring with Incomplete Data: An Integrated Variational
Autoencoder and Distance Metric Framework
|
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample's deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.
|
http://arxiv.org/pdf/2404.05891v2
|
[
"Maryam Ahang",
"Mostafa Abbasi",
"Todd Charter",
"Homayoun Najjaran"
] |
2024-06-27T21:54:40Z
|
2024-04-08T22:20:23Z
|
2406.19532
|
Dataless Quadratic Neural Networks for the Maximum Independent Set
Problem
|
Combinatorial Optimization (CO) plays a crucial role in addressing various significant problems, among them the challenging Maximum Independent Set (MIS) problem. In light of recent advancements in deep learning methods, efforts have been directed towards leveraging data-driven learning approaches, typically rooted in supervised learning and reinforcement learning, to tackle the NP-hard MIS problem. However, these approaches rely on labeled datasets, exhibit weak generalization, and often depend on problem-specific heuristics. Recently, ReLU-based dataless neural networks were introduced to address combinatorial optimization problems. This paper introduces a novel dataless quadratic neural network formulation, featuring a continuous quadratic relaxation for the MIS problem. Notably, our method eliminates the need for training data by treating the given MIS instance as a trainable entity. More specifically, the graph structure and constraints of the MIS instance are used to define the structure and parameters of the neural network such that training it on a fixed input provides a solution to the problem, thereby setting it apart from traditional supervised or reinforcement learning approaches. By employing a gradient-based optimization algorithm like ADAM and leveraging an efficient off-the-shelf GPU parallel implementation, our straightforward yet effective approach demonstrates competitive or superior performance compared to state-of-the-art learning-based methods. Another significant advantage of our approach is that, unlike exact and heuristic solvers, the running time of our method scales only with the number of nodes in the graph, not the number of edges.
|
http://arxiv.org/pdf/2406.19532v1
|
[
"Ismail Alkhouri",
"Cedric Le Denmat",
"Yingjie Li",
"Cunxi Yu",
"Jia Liu",
"Rongrong Wang",
"Alvaro Velasquez"
] |
2024-06-27T21:12:48Z
|
2024-06-27T21:12:48Z
|
2406.19531
|
Forward and Backward State Abstractions for Off-policy Evaluation
|
Off-policy evaluation (OPE) is crucial for evaluating a target policy's impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challenging.This paper studies state abstractions-originally designed for policy learning-in the context of OPE. Our contributions are three-fold: (i) We define a set of irrelevance conditions central to learning state abstractions for OPE. (ii) We derive sufficient conditions for achieving irrelevance in Q-functions and marginalized importance sampling ratios, the latter obtained by constructing a time-reversed Markov decision process (MDP) based on the observed MDP. (iii) We propose a novel two-step procedure that sequentially projects the original state space into a smaller space, which substantially simplify the sample complexity of OPE arising from high cardinality.
|
http://arxiv.org/pdf/2406.19531v1
|
[
"Meiling Hao",
"Pingfan Su",
"Liyuan Hu",
"Zoltan Szabo",
"Qingyuan Zhao",
"Chengchun Shi"
] |
2024-06-27T21:12:26Z
|
2024-06-27T21:12:26Z
|
2406.19526
|
TocBERT: Medical Document Structure Extraction Using Bidirectional
Transformers
|
Text segmentation holds paramount importance in the field of Natural Language Processing (NLP). It plays an important role in several NLP downstream tasks like information retrieval and document summarization. In this work, we propose a new solution, namely TocBERT, for segmenting texts using bidirectional transformers. TocBERT represents a supervised solution trained on the detection of titles and sub-titles from their semantic representations. This task was formulated as a named entity recognition (NER) problem. The solution has been applied on a medical text segmentation use-case where the Bio-ClinicalBERT model is fine-tuned to segment discharge summaries of the MIMIC-III dataset. The performance of TocBERT has been evaluated on a human-labeled ground truth corpus of 250 notes. It achieved an F1-score of 84.6% when evaluated on a linear text segmentation problem and 72.8% on a hierarchical text segmentation problem. It outperformed a carefully designed rule-based solution, particularly in distinguishing titles from subtitles.
|
http://arxiv.org/pdf/2406.19526v1
|
[
"Majd Saleh",
"Sarra Baghdadi",
"Stéphane Paquelet"
] |
2024-06-27T20:56:57Z
|
2024-06-27T20:56:57Z
|
2312.00246
|
Directions of Curvature as an Explanation for Loss of Plasticity
|
Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from new experience. Despite being empirically observed in several problem settings, little is understood about the mechanisms that lead to loss of plasticity. In this paper, we offer a consistent explanation for loss of plasticity: Neural networks lose directions of curvature during training and that loss of plasticity can be attributed to this reduction in curvature. To support such a claim, we provide a systematic investigation of loss of plasticity across continual learning tasks using MNIST, CIFAR-10 and ImageNet. Our findings illustrate that loss of curvature directions coincides with loss of plasticity, while also showing that previous explanations are insufficient to explain loss of plasticity in all settings. Lastly, we show that regularizers which mitigate loss of plasticity also preserve curvature, motivating a simple distributional regularizer that proves to be effective across the problem settings we considered.
|
http://arxiv.org/pdf/2312.00246v3
|
[
"Alex Lewandowski",
"Haruto Tanaka",
"Dale Schuurmans",
"Marlos C. Machado"
] |
2024-06-27T20:51:56Z
|
2023-11-30T23:24:45Z
|
2406.19524
|
Bayesian calibration of stochastic agent based model via random forest
|
Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and environments. However, these models are usually stochastic and highly parametrized, requiring precise calibration for predictive performance. When considering realistic numbers of agents and properly accounting for stochasticity, this high dimensional calibration can be computationally prohibitive. This paper presents a random forest based surrogate modeling technique to accelerate the evaluation of ABMs and demonstrates its use to calibrate an epidemiological ABM named CityCOVID via Markov chain Monte Carlo (MCMC). The technique is first outlined in the context of CityCOVID's quantities of interest, namely hospitalizations and deaths, by exploring dimensionality reduction via temporal decomposition with principal component analysis (PCA) and via sensitivity analysis. The calibration problem is then presented and samples are generated to best match COVID-19 hospitalization and death numbers in Chicago from March to June in 2020. These results are compared with previous approximate Bayesian calibration (IMABC) results and their predictive performance is analyzed showing improved performance with a reduction in computation.
|
http://arxiv.org/pdf/2406.19524v1
|
[
"Connor Robertson",
"Cosmin Safta",
"Nicholson Collier",
"Jonathan Ozik",
"Jaideep Ray"
] |
2024-06-27T20:50:06Z
|
2024-06-27T20:50:06Z
|
2406.19522
|
Reliable edge machine learning hardware for scientific applications
|
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.
|
http://arxiv.org/pdf/2406.19522v1
|
[
"Tommaso Baldi",
"Javier Campos",
"Ben Hawks",
"Jennifer Ngadiuba",
"Nhan Tran",
"Daniel Diaz",
"Javier Duarte",
"Ryan Kastner",
"Andres Meza",
"Melissa Quinnan",
"Olivia Weng",
"Caleb Geniesse",
"Amir Gholami",
"Michael W. Mahoney",
"Vladimir Loncar",
"Philip Harris",
"Joshua Agar",
"Shuyu Qin"
] |
2024-06-27T20:45:08Z
|
2024-06-27T20:45:08Z
|
2406.19507
|
Too Good to be True? Turn Any Model Differentially Private With
DP-Weights
|
Imagine training a machine learning model with Differentially Private Stochastic Gradient Descent (DP-SGD), only to discover post-training that the noise level was either too high, crippling your model's utility, or too low, compromising privacy. The dreaded realization hits: you must start the lengthy training process from scratch. But what if you could avoid this retraining nightmare? In this study, we introduce a groundbreaking approach (to our knowledge) that applies differential privacy noise to the model's weights after training. We offer a comprehensive mathematical proof for this novel approach's privacy bounds, use formal methods to validate its privacy guarantees, and empirically evaluate its effectiveness using membership inference attacks and performance evaluations. This method allows for a single training run, followed by post-hoc noise adjustments to achieve optimal privacy-utility trade-offs. We compare this novel fine-tuned model (DP-Weights model) to a traditional DP-SGD model, demonstrating that our approach yields statistically similar performance and privacy guarantees. Our results validate the efficacy of post-training noise application, promising significant time savings and flexibility in fine-tuning differential privacy parameters, making it a practical alternative for deploying differentially private models in real-world scenarios.
|
http://arxiv.org/pdf/2406.19507v1
|
[
"David Zagardo"
] |
2024-06-27T19:58:11Z
|
2024-06-27T19:58:11Z
|
2406.19501
|
Monitoring Latent World States in Language Models with Propositional
Probes
|
Language models are susceptible to bias, sycophancy, backdoors, and other tendencies that lead to unfaithful responses to the input context. Interpreting internal states of language models could help monitor and correct unfaithful behavior. We hypothesize that language models represent their input contexts in a latent world model, and seek to extract this latent world state from the activations. We do so with 'propositional probes', which compositionally probe tokens for lexical information and bind them into logical propositions representing the world state. For example, given the input context ''Greg is a nurse. Laura is a physicist.'', we decode the propositions ''WorksAs(Greg, nurse)'' and ''WorksAs(Laura, physicist)'' from the model's activations. Key to this is identifying a 'binding subspace' in which bound tokens have high similarity (''Greg'' and ''nurse'') but unbound ones do not (''Greg'' and ''physicist''). We validate propositional probes in a closed-world setting with finitely many predicates and properties. Despite being trained on simple templated contexts, propositional probes generalize to contexts rewritten as short stories and translated to Spanish. Moreover, we find that in three settings where language models respond unfaithfully to the input context -- prompt injections, backdoor attacks, and gender bias -- the decoded propositions remain faithful. This suggests that language models often encode a faithful world model but decode it unfaithfully, which motivates the search for better interpretability tools for monitoring LMs.
|
http://arxiv.org/pdf/2406.19501v1
|
[
"Jiahai Feng",
"Stuart Russell",
"Jacob Steinhardt"
] |
2024-06-27T19:28:43Z
|
2024-06-27T19:28:43Z
|
2311.00964
|
On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for
Fintech Applications
|
Rules are widely used in Fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. In practice, a two-stage framework of fraud prevention decision rule set mining is usually employed in large Fintech institutions; Stage 1 generates a potentially large pool of rules and Stage 2 aims to produce a refined rule subset according to some criteria (typically based on precision and recall). This paper focuses on improving the flexibility and efficacy of this two-stage framework, and is concerned with finding high-quality rule subsets in a bi-objective space (such as precision and recall). To this end, we first introduce a novel algorithm called SpectralRules that directly generates a compact pool of rules in Stage 1 with high diversity. We empirically find such diversity improves the quality of the final rule subset. In addition, we introduce an intermediate stage between Stage 1 and 2 that adopts the concept of Pareto optimality and aims to find a set of non-dominated rule subsets, which constitutes a Pareto front. This intermediate stage greatly simplifies the selection criteria and increases the flexibility of Stage 2. For this intermediate stage, we propose a heuristic-based framework called PORS and we identify that the core of PORS is the problem of solution selection on the front (SSF). We provide a systematic categorization of the SSF problem and a thorough empirical evaluation of various SSF methods on both public and proprietary datasets. On two real application scenarios within Alipay, we demonstrate the advantages of our proposed methodology over existing work.
|
http://arxiv.org/abs/2311.00964v3
|
[
"Chengyao Wen",
"Yin Lou"
] |
2024-06-27T19:07:30Z
|
2023-11-02T03:18:40Z
|
2402.02681
|
Equivariant Symmetry Breaking Sets
|
Equivariant neural networks (ENNs) have been shown to be extremely effective in applications involving underlying symmetries. By construction ENNs cannot produce lower symmetry outputs given a higher symmetry input. However, symmetry breaking occurs in many physical systems and we may obtain a less symmetric stable state from an initial highly symmetric one. Hence, it is imperative that we understand how to systematically break symmetry in ENNs. In this work, we propose a novel symmetry breaking framework that is fully equivariant and is the first which fully addresses spontaneous symmetry breaking. We emphasize that our approach is general and applicable to equivariance under any group. To achieve this, we introduce the idea of symmetry breaking sets (SBS). Rather than redesign existing networks, we design sets of symmetry breaking objects which we feed into our network based on the symmetry of our inputs and outputs. We show there is a natural way to define equivariance on these sets, which gives an additional constraint. Minimizing the size of these sets equates to data efficiency. We prove that minimizing these sets translates to a well studied group theory problem, and tabulate solutions to this problem for the point groups. Finally, we provide some examples of symmetry breaking to demonstrate how our approach works in practice.
|
http://arxiv.org/pdf/2402.02681v2
|
[
"YuQing Xie",
"Tess Smidt"
] |
2024-06-27T19:06:32Z
|
2024-02-05T02:35:11Z
|
2406.19486
|
LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models
|
In prompt tuning, a prefix or suffix text is added to the prompt, and the embeddings (soft prompts) or token indices (hard prompts) of the prefix/suffix are optimized to gain more control over language models for specific tasks. This approach eliminates the need for hand-crafted prompt engineering or explicit model fine-tuning. Prompt tuning is significantly more parameter-efficient than model fine-tuning, as it involves optimizing partial inputs of language models to produce desired outputs. In this work, we aim to further reduce the amount of trainable parameters required for a language model to perform well on specific tasks. We propose Low-rank Prompt Tuning (LoPT), a low-rank model for prompts that achieves efficient prompt optimization. The proposed method demonstrates similar outcomes to full parameter prompt tuning while reducing the number of trainable parameters by a factor of 5. It also provides promising results compared to the state-of-the-art methods that would require 10 to 20 times more parameters.
|
http://arxiv.org/pdf/2406.19486v1
|
[
"Shouchang Guo",
"Sonam Damani",
"Keng-hao Chang"
] |
2024-06-27T19:02:41Z
|
2024-06-27T19:02:41Z
|
2312.06440
|
Towards A Flexible Accuracy-Oriented Deep Learning Module Inference
Latency Prediction Framework for Adaptive Optimization Algorithms
|
With the rapid development of Deep Learning, more and more applications on the cloud and edge tend to utilize large DNN (Deep Neural Network) models for improved task execution efficiency as well as decision-making quality. Due to memory constraints, models are commonly optimized using compression, pruning, and partitioning algorithms to become deployable onto resource-constrained devices. As the conditions in the computational platform change dynamically, the deployed optimization algorithms should accordingly adapt their solutions. To perform frequent evaluations of these solutions in a timely fashion, RMs (Regression Models) are commonly trained to predict the relevant solution quality metrics, such as the resulted DNN module inference latency, which is the focus of this paper. Existing prediction frameworks specify different RM training workflows, but none of them allow flexible configurations of the input parameters (e.g., batch size, device utilization rate) and of the selected RMs for different modules. In this paper, a deep learning module inference latency prediction framework is proposed, which i) hosts a set of customizable input parameters to train multiple different RMs per DNN module (e.g., convolutional layer) with self-generated datasets, and ii) automatically selects a set of trained RMs leading to the highest possible overall prediction accuracy, while keeping the prediction time / space consumption as low as possible. Furthermore, a new RM, namely MEDN (Multi-task Encoder-Decoder Network), is proposed as an alternative solution. Comprehensive experiment results show that MEDN is fast and lightweight, and capable of achieving the highest overall prediction accuracy and R-squared value. The Time/Space-efficient Auto-selection algorithm also manages to improve the overall accuracy by 2.5% and R-squared by 0.39%, compared to the MEDN single-selection scheme.
|
http://arxiv.org/abs/2312.06440v2
|
[
"Jingran Shen",
"Nikos Tziritas",
"Georgios Theodoropoulos"
] |
2024-06-27T19:01:32Z
|
2023-12-11T15:15:48Z
|
2406.19477
|
Multi-agent Cooperative Games Using Belief Map Assisted Training
|
In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model's complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent's hidden state to provide a symbolic representation of the agent's understanding of the environment and other agent's status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34.62% fewer steps on average.
|
http://arxiv.org/abs/2406.19477v1
|
[
"Qinwei Huang",
"Chen Luo",
"Alex B. Wu",
"Simon Khan",
"Hai Li",
"Qinru Qiu"
] |
2024-06-27T18:40:55Z
|
2024-06-27T18:40:55Z
|
2305.14752
|
A New Era in Software Security: Towards Self-Healing Software via Large
Language Models and Formal Verification
|
This paper introduces an innovative approach that combines Large Language Models (LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially, we employ Bounded Model Checking (BMC) to identify vulnerabilities and extract counterexamples. These counterexamples are supported by mathematical proofs and the stack trace of the vulnerabilities. Using a specially designed prompt, we combine the original source code with the identified vulnerability, including its stack trace and counterexample that specifies the line number and error type. This combined information is then fed into an LLM, which is instructed to attempt to fix the code. The new code is subsequently verified again using BMC to ensure the fix succeeded. We present the ESBMC-AI framework as a proof of concept, leveraging the well-recognized and industry-adopted Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained transformer model to detect and fix errors in C programs, particularly in critical software components. We evaluated our approach on 50,000 C programs randomly selected from the FormAI dataset with their respective vulnerability classifications. Our results demonstrate ESBMC-AI's capability to automate the detection and repair of issues such as buffer overflow, arithmetic overflow, and pointer dereference failures with high accuracy. ESBMC-AI is a pioneering initiative, integrating LLMs with BMC techniques, offering potential integration into the continuous integration and deployment (CI/CD) process within the software development lifecycle.
|
http://arxiv.org/pdf/2305.14752v2
|
[
"Norbert Tihanyi",
"Ridhi Jain",
"Yiannis Charalambous",
"Mohamed Amine Ferrag",
"Youcheng Sun",
"Lucas C. Cordeiro"
] |
2024-06-27T18:40:19Z
|
2023-05-24T05:54:10Z
|
2406.19475
|
Stochastic First-Order Methods with Non-smooth and Non-Euclidean
Proximal Terms for Nonconvex High-Dimensional Stochastic Optimization
|
When the nonconvex problem is complicated by stochasticity, the sample complexity of stochastic first-order methods may depend linearly on the problem dimension, which is undesirable for large-scale problems. In this work, we propose dimension-insensitive stochastic first-order methods (DISFOMs) to address nonconvex optimization with expected-valued objective function. Our algorithms allow for non-Euclidean and non-smooth distance functions as the proximal terms. Under mild assumptions, we show that DISFOM using minibatches to estimate the gradient enjoys sample complexity of $ mathcal{O} ( (log d) / epsilon^4 ) $ to obtain an $epsilon$-stationary point. Furthermore, we prove that DISFOM employing variance reduction can sharpen this bound to $mathcal{O} ( (log d)^{2/3}/epsilon^{10/3} )$, which perhaps leads to the best-known sample complexity result in terms of $d$. We provide two choices of the non-smooth distance functions, both of which allow for closed-form solutions to the proximal step. Numerical experiments are conducted to illustrate the dimension insensitive property of the proposed frameworks.
|
http://arxiv.org/pdf/2406.19475v1
|
[
"Yue Xie",
"Jiawen Bi",
"Hongcheng Liu"
] |
2024-06-27T18:38:42Z
|
2024-06-27T18:38:42Z
|
2406.17073
|
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing
with the Data Imbalance in Graph Neural Networks
|
Although many real-world applications, such as disease prediction, and fault detection suffer from class imbalance, most existing graph-based classification methods ignore the skewness of the distribution of classes; therefore, tend to be biased towards the majority class(es). Conventional methods typically tackle this problem through the assignment of weights to each one of the class samples based on a function of their loss, which can lead to over-fitting on outliers. In this paper, we propose a meta-learning algorithm, named Meta-GCN, for adaptively learning the example weights by simultaneously minimizing the unbiased meta-data set loss and optimizing the model weights through the use of a small unbiased meta-data set. Through experiments, we have shown that Meta-GCN outperforms state-of-the-art frameworks and other baselines in terms of accuracy, the area under the receiver operating characteristic (AUC-ROC) curve, and macro F1-Score for classification tasks on two different datasets.
|
http://arxiv.org/abs/2406.17073v2
|
[
"Mahdi Mohammadizadeh",
"Arash Mozhdehi",
"Yani Ioannou",
"Xin Wang"
] |
2024-06-27T18:15:16Z
|
2024-06-24T18:59:24Z
|
2406.19384
|
The Remarkable Robustness of LLMs: Stages of Inference?
|
We demonstrate and investigate the remarkable robustness of Large Language Models by deleting and swapping adjacent layers. We find that deleting and swapping interventions retain 72-95% of the original model's prediction accuracy without fine-tuning, whereas models with more layers exhibit more robustness. Based on the results of the layer-wise intervention and further experiments, we hypothesize the existence of four universal stages of inference across eight different models: detokenization, feature engineering, prediction ensembling, and residual sharpening. The first stage integrates local information, lifting raw token representations into higher-level contextual representations. Next is the iterative refinement of task and entity-specific features. Then, the second half of the model begins with a phase transition, where hidden representations align more with the vocabulary space due to specialized model components. Finally, the last layer sharpens the following token distribution by eliminating obsolete features that add noise to the prediction.
|
http://arxiv.org/pdf/2406.19384v1
|
[
"Vedang Lad",
"Wes Gurnee",
"Max Tegmark"
] |
2024-06-27T17:57:03Z
|
2024-06-27T17:57:03Z
|
2406.19370
|
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept
Space
|
Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However, fundamental questions remain: what determines the concepts a model learns, the order in which it learns them, and its ability to manipulate those concepts? To address these questions, we propose analyzing a model's learning dynamics via a framework we call the concept space, where each axis represents an independent concept underlying the data generating process. By characterizing learning dynamics in this space, we identify how the speed at which a concept is learned, and hence the order of concept learning, is controlled by properties of the data we term concept signal. Further, we observe moments of sudden turns in the direction of a model's learning dynamics in concept space. Surprisingly, these points precisely correspond to the emergence of hidden capabilities, i.e., where latent interventions show the model possesses the capability to manipulate a concept, but these capabilities cannot yet be elicited via naive input prompting. While our results focus on synthetically defined toy datasets, we hypothesize a general claim on emergence of hidden capabilities may hold: generative models possess latent capabilities that emerge suddenly and consistently during training, though a model might not exhibit these capabilities under naive input prompting.
|
http://arxiv.org/pdf/2406.19370v1
|
[
"Core Francisco Park",
"Maya Okawa",
"Andrew Lee",
"Ekdeep Singh Lubana",
"Hidenori Tanaka"
] |
2024-06-27T17:50:05Z
|
2024-06-27T17:50:05Z
|
2407.00121
|
Granite-Function Calling Model: Introducing Function Calling Abilities
via Multi-task Learning of Granular Tasks
|
Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challenging benchmarks like SWE-Bench and Agent-Bench. However, to realize the true potential of LLMs as autonomous agents, they must learn to identify, call, and interact with external tools and application program interfaces (APIs) to complete complex tasks. These tasks together are termed function calling. Endowing LLMs with function calling abilities leads to a myriad of advantages, such as access to current and domain-specific information in databases and knowledge sources, and the ability to outsource tasks that can be reliably performed by tools, e.g., a Python interpreter or calculator. While there has been significant progress in function calling with LLMs, there is still a dearth of open models that perform on par with proprietary LLMs like GPT, Claude, and Gemini. Therefore, in this work, we introduce the GRANITE-20B-FUNCTIONCALLING model under an Apache 2.0 license. The model is trained using a multi-task training approach on seven fundamental tasks encompassed in function calling, those being Nested Function Calling, Function Chaining, Parallel Functions, Function Name Detection, Parameter-Value Pair Detection, Next-Best Function, and Response Generation. We present a comprehensive evaluation on multiple out-of-domain datasets comparing GRANITE-20B-FUNCTIONCALLING to more than 15 other best proprietary and open models. GRANITE-20B-FUNCTIONCALLING provides the best performance among all open models on the Berkeley Function Calling Leaderboard and fourth overall. As a result of the diverse tasks and datasets used for training our model, we show that GRANITE-20B-FUNCTIONCALLING has better generalizability on multiple tasks in seven different evaluation datasets.
|
http://arxiv.org/pdf/2407.00121v1
|
[
"Ibrahim Abdelaziz",
"Kinjal Basu",
"Mayank Agarwal",
"Sadhana Kumaravel",
"Matthew Stallone",
"Rameswar Panda",
"Yara Rizk",
"GP Bhargav",
"Maxwell Crouse",
"Chulaka Gunasekara",
"Shajith Ikbal",
"Sachin Joshi",
"Hima Karanam",
"Vineet Kumar",
"Asim Munawar",
"Sumit Neelam",
"Dinesh Raghu",
"Udit Sharma",
"Adriana Meza Soria",
"Dheeraj Sreedhar",
"Praveen Venkateswaran",
"Merve Unuvar",
"David Cox",
"Salim Roukos",
"Luis Lastras",
"Pavan Kapanipathi"
] |
2024-06-27T17:47:26Z
|
2024-06-27T17:47:26Z
|
2406.19356
|
DiVERT: Distractor Generation with Variational Errors Represented as
Text for Math Multiple-choice Questions
|
High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real students is difficult. Meanwhile, automated distractor generation, even with the help of large language models (LLMs), remains challenging for subjects like math. It is crucial to not only identify plausible distractors but also understand the error behind them. In this paper, we introduce DiVERT (Distractor Generation with Variational Errors Represented as Text), a novel variational approach that learns an interpretable representation of errors behind distractors in math MCQs. Through experiments on a real-world math MCQ dataset with 1,434 questions used by hundreds of thousands of students, we show that DiVERT, despite using a base open-source LLM with 7B parameters, outperforms state-of-the-art approaches using GPT-4o on downstream distractor generation. We also conduct a human evaluation with math educators and find that DiVERT leads to error labels that are of comparable quality to human-authored ones.
|
http://arxiv.org/pdf/2406.19356v1
|
[
"Nigel Fernandez",
"Alexander Scarlatos",
"Simon Woodhead",
"Andrew Lan"
] |
2024-06-27T17:37:31Z
|
2024-06-27T17:37:31Z
|
2403.13040
|
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
|
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow.
|
http://arxiv.org/abs/2403.13040v2
|
[
"Hang Jung Ling",
"Salomé Bru",
"Julia Puig",
"Florian Vixège",
"Simon Mendez",
"Franck Nicoud",
"Pierre-Yves Courand",
"Olivier Bernard",
"Damien Garcia"
] |
2024-06-27T17:27:13Z
|
2024-03-19T17:35:17Z
|
2407.09551
|
Diminishing Stereotype Bias in Image Generation Model using
Reinforcemenlent Learning Feedback
|
This study addresses gender bias in image generation models using Reinforcement Learning from Artificial Intelligence Feedback (RLAIF) with a novel Denoising Diffusion Policy Optimization (DDPO) pipeline. By employing a pretrained stable diffusion model and a highly accurate gender classification Transformer, the research introduces two reward functions: Rshift for shifting gender imbalances, and Rbalance for achieving and maintaining gender balance. Experiments demonstrate the effectiveness of this approach in mitigating bias without compromising image quality or requiring additional data or prompt modifications. While focusing on gender bias, this work establishes a foundation for addressing various forms of bias in AI systems, emphasizing the need for responsible AI development. Future research directions include extending the methodology to other bias types, enhancing the RLAIF pipeline's robustness, and exploring multi-prompt fine-tuning to further advance fairness and inclusivity in AI.
|
http://arxiv.org/pdf/2407.09551v1
|
[
"Xin Chen",
"Virgile Foussereau"
] |
2024-06-27T17:18:58Z
|
2024-06-27T17:18:58Z
|
2405.16755
|
CHESS: Contextual Harnessing for Efficient SQL Synthesis
|
Utilizing large language models (LLMs) for transforming natural language questions into SQL queries (text-to-SQL) is a promising yet challenging approach, particularly when applied to real-world databases with complex and extensive schemas. In particular, effectively incorporating data catalogs and database values for SQL generation remains an obstacle, leading to suboptimal solutions. We address this problem by proposing a new pipeline that effectively retrieves relevant data and context, selects an efficient schema, and synthesizes correct and efficient SQL queries. To increase retrieval precision, our pipeline introduces a hierarchical retrieval method leveraging model-generated keywords, locality-sensitive hashing indexing, and vector databases. Additionally, we have developed an adaptive schema pruning technique that adjusts based on the complexity of the problem and the model's context size. Our approach generalizes to both frontier proprietary models like GPT-4 and open-source models such as Llama-3-70B. Through a series of ablation studies, we demonstrate the effectiveness of each component of our pipeline and its impact on the end-to-end performance. Our method achieves new state-of-the-art performance on the cross-domain challenging BIRD dataset.
|
http://arxiv.org/pdf/2405.16755v2
|
[
"Shayan Talaei",
"Mohammadreza Pourreza",
"Yu-Chen Chang",
"Azalia Mirhoseini",
"Amin Saberi"
] |
2024-06-27T17:13:32Z
|
2024-05-27T01:54:16Z
|
2406.19328
|
Subtractive Training for Music Stem Insertion using Latent Diffusion
Models
|
We present Subtractive Training, a simple and novel method for synthesizing individual musical instrument stems given other instruments as context. This method pairs a dataset of complete music mixes with 1) a variant of the dataset lacking a specific stem, and 2) LLM-generated instructions describing how the missing stem should be reintroduced. We then fine-tune a pretrained text-to-audio diffusion model to generate the missing instrument stem, guided by both the existing stems and the text instruction. Our results demonstrate Subtractive Training's efficacy in creating authentic drum stems that seamlessly blend with the existing tracks. We also show that we can use the text instruction to control the generation of the inserted stem in terms of rhythm, dynamics, and genre, allowing us to modify the style of a single instrument in a full song while keeping the remaining instruments the same. Lastly, we extend this technique to MIDI formats, successfully generating compatible bass, drum, and guitar parts for incomplete arrangements.
|
http://arxiv.org/pdf/2406.19328v1
|
[
"Ivan Villa-Renteria",
"Mason L. Wang",
"Zachary Shah",
"Zhe Li",
"Soohyun Kim",
"Neelesh Ramachandran",
"Mert Pilanci"
] |
2024-06-27T16:59:14Z
|
2024-06-27T16:59:14Z
|
2406.12373
|
WebCanvas: Benchmarking Web Agents in Online Environments
|
For web agents to be practically useful, they must adapt to the continuously evolving web environment characterized by frequent updates to user interfaces and content. However, most existing benchmarks only capture the static aspects of the web. To bridge this gap, we introduce WebCanvas, an innovative online evaluation framework for web agents that effectively addresses the dynamic nature of web interactions. WebCanvas contains three main components to facilitate realistic assessments: (1) A novel evaluation metric which reliably capture critical intermediate actions or states necessary for task completions while disregarding noise caused by insignificant events or changed web-elements. (2) A benchmark dataset called Mind2Web-Live, a refined version of original Mind2Web static dataset containing 542 tasks with 2439 intermediate evaluation states; (3) Lightweight and generalizable annotation tools and testing pipelines that enables the community to collect and maintain the high-quality, up-to-date dataset. Building on WebCanvas, we open-source an agent framework with extensible modules for reasoning, providing a foundation for the community to conduct online inference and evaluations. Our best-performing agent achieves a task success rate of 23.1% and a task completion rate of 48.8% on the Mind2Web-Live test set. Additionally, we analyze the performance discrepancies across various websites, domains, and experimental environments. We encourage the community to contribute further insights on online agent evaluation, thereby advancing this field of research.
|
http://arxiv.org/pdf/2406.12373v2
|
[
"Yichen Pan",
"Dehan Kong",
"Sida Zhou",
"Cheng Cui",
"Yifei Leng",
"Bing Jiang",
"Hangyu Liu",
"Yanyi Shang",
"Shuyan Zhou",
"Tongshuang Wu",
"Zhengyang Wu"
] |
2024-06-27T16:56:13Z
|
2024-06-18T07:58:33Z
|
2406.19320
|
Efficient World Models with Context-Aware Tokenization
|
Scaling up deep Reinforcement Learning (RL) methods presents a significant challenge. Following developments in generative modelling, model-based RL positions itself as a strong contender. Recent advances in sequence modelling have led to effective transformer-based world models, albeit at the price of heavy computations due to the long sequences of tokens required to accurately simulate environments. In this work, we propose $Delta$-IRIS, a new agent with a world model architecture composed of a discrete autoencoder that encodes stochastic deltas between time steps and an autoregressive transformer that predicts future deltas by summarizing the current state of the world with continuous tokens. In the Crafter benchmark, $Delta$-IRIS sets a new state of the art at multiple frame budgets, while being an order of magnitude faster to train than previous attention-based approaches. We release our code and models at https://github.com/vmicheli/delta-iris.
|
http://arxiv.org/pdf/2406.19320v1
|
[
"Vincent Micheli",
"Eloi Alonso",
"François Fleuret"
] |
2024-06-27T16:54:12Z
|
2024-06-27T16:54:12Z
|
2406.19317
|
Jump Starting Bandits with LLM-Generated Prior Knowledge
|
We present substantial evidence demonstrating the benefits of integrating Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework. Contextual bandits have been widely used in recommendation systems to generate personalized suggestions based on user-specific contexts. We show that LLMs, pre-trained on extensive corpora rich in human knowledge and preferences, can simulate human behaviours well enough to jump-start contextual multi-armed bandits to reduce online learning regret. We propose an initialization algorithm for contextual bandits by prompting LLMs to produce a pre-training dataset of approximate human preferences for the bandit. This significantly reduces online learning regret and data-gathering costs for training such models. Our approach is validated empirically through two sets of experiments with different bandit setups: one which utilizes LLMs to serve as an oracle and a real-world experiment utilizing data from a conjoint survey experiment.
|
http://arxiv.org/pdf/2406.19317v1
|
[
"Parand A. Alamdari",
"Yanshuai Cao",
"Kevin H. Wilson"
] |
2024-06-27T16:52:19Z
|
2024-06-27T16:52:19Z
|
2303.13775
|
GSplit: Scaling Graph Neural Network Training on Large Graphs via
Split-Parallelism
|
Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini-batch training is commonly used to train GNNs on large graphs, and data parallelism is the standard approach to scale mini-batch training across multiple GPUs. One of the major performance costs in GNN training is the loading of input features, which prevents GPUs from being fully utilized. In this paper, we argue that this problem is exacerbated by redundancies that are inherent to the data parallel approach. To address this issue, we introduce a hybrid parallel mini-batch training paradigm called split parallelism. Split parallelism avoids redundant data loads and splits the sampling and training of each mini-batch across multiple GPUs online, at each iteration, using a lightweight splitting algorithm. We implement split parallelism in GSplit and show that it outperforms state-of-the-art mini-batch training systems like DGL, Quiver, and $P^3$.
|
http://arxiv.org/pdf/2303.13775v2
|
[
"Sandeep Polisetty",
"Juelin Liu",
"Kobi Falus",
"Yi Ren Fung",
"Seung-Hwan Lim",
"Hui Guan",
"Marco Serafini"
] |
2024-06-27T16:51:27Z
|
2023-03-24T03:28:05Z
|
2407.00120
|
Automated Web-Based Malaria Detection System with Machine Learning and
Deep Learning Techniques
|
Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malaria-infected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, recall, and F1-score. The test results showed that deep CNNs achieved the highest accuracy -- 97%, followed by Xception with an accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can be accessed through a web interface, where users can upload blood smear images for malaria detection.
|
http://arxiv.org/pdf/2407.00120v1
|
[
"Abraham G Taye",
"Sador Yemane",
"Eshetu Negash",
"Yared Minwuyelet",
"Moges Abebe",
"Melkamu Hunegnaw Asmare"
] |
2024-06-27T16:50:36Z
|
2024-06-27T16:50:36Z
|
2406.19314
|
LiveBench: A Challenging, Contamination-Free LLM Benchmark
|
Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource new prompts and evaluations from human or LLM judges; however, these can introduce significant biases, and break down when scoring hard questions. In this work, we introduce a new benchmark for LLMs designed to be immune to both test set contamination and the pitfalls of LLM judging and human crowdsourcing. We release LiveBench, the first benchmark that (1) contains frequently-updated questions from recent information sources, (2) scores answers automatically according to objective ground-truth values, and (3) contains a wide variety of challenging tasks, spanning math, coding, reasoning, language, instruction following, and data analysis. To achieve this, LiveBench contains questions that are based on recently-released math competitions, arXiv papers, news articles, and datasets, and it contains harder, contamination-free versions of tasks from previous benchmarks such as Big-Bench Hard, AMPS, and IFEval. We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 110B in size. LiveBench is difficult, with top models achieving below 65% accuracy. We release all questions, code, and model answers. Questions will be added and updated on a monthly basis, and we will release new tasks and harder versions of tasks over time so that LiveBench can distinguish between the capabilities of LLMs as they improve in the future. We welcome community engagement and collaboration for expanding the benchmark tasks and models.
|
http://arxiv.org/pdf/2406.19314v1
|
[
"Colin White",
"Samuel Dooley",
"Manley Roberts",
"Arka Pal",
"Ben Feuer",
"Siddhartha Jain",
"Ravid Shwartz-Ziv",
"Neel Jain",
"Khalid Saifullah",
"Siddartha Naidu",
"Chinmay Hegde",
"Yann LeCun",
"Tom Goldstein",
"Willie Neiswanger",
"Micah Goldblum"
] |
2024-06-27T16:47:42Z
|
2024-06-27T16:47:42Z
|
2312.17293
|
$μ$GUIDE: a framework for quantitative imaging via generalized
uncertainty-driven inference using deep learning
|
This work proposes $mu$GUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or MRI signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, $mu$GUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.
|
http://arxiv.org/pdf/2312.17293v3
|
[
"Maëliss Jallais",
"Marco Palombo"
] |
2024-06-27T16:38:18Z
|
2023-12-28T13:59:43Z
|
2403.08819
|
Thermometer: Towards Universal Calibration for Large Language Models
|
We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational requirements of LLMs as from their versatility, which allows them to be applied to diverse tasks. Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs. THERMOMETER learns an auxiliary model, given data from multiple tasks, for calibrating a LLM. It is computationally efficient, preserves the accuracy of the LLM, and produces better-calibrated responses for new tasks. Extensive empirical evaluations across various benchmarks demonstrate the effectiveness of the proposed method.
|
http://arxiv.org/pdf/2403.08819v2
|
[
"Maohao Shen",
"Subhro Das",
"Kristjan Greenewald",
"Prasanna Sattigeri",
"Gregory Wornell",
"Soumya Ghosh"
] |
2024-06-27T16:30:32Z
|
2024-02-20T04:13:48Z
|
2406.19302
|
Mapping Land Naturalness from Sentinel-2 using Deep Contextual and
Geographical Priors
|
In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to observe and measure these effects has become crucial for understanding and combating climate change. Aiming to map land naturalness on the continuum of modern human pressure, we have developed a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors, represented by corresponding coordinate information and broader contextual information, including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance in mapping land naturalness from Sentinel-2 data, a type of multi-spectral optical satellite imagery. Recognizing that our protective measures are only as effective as our understanding of the ecosystem, quantifying naturalness serves as a crucial step toward enhancing our environmental stewardship.
|
http://arxiv.org/pdf/2406.19302v1
|
[
"Burak Ekim",
"Michael Schmitt"
] |
2024-06-27T16:17:33Z
|
2024-06-27T16:17:33Z
|
2406.19301
|
MCNC: Manifold Constrained Network Compression
|
The outstanding performance of large foundational models across diverse tasks-from computer vision to speech and natural language processing-has significantly increased their demand. However, storing and transmitting these models pose significant challenges due to their massive size (e.g., 350GB for GPT-3). Recent literature has focused on compressing the original weights or reducing the number of parameters required for fine-tuning these models. These compression methods typically involve constraining the parameter space, for example, through low-rank reparametrization (e.g., LoRA) or quantization (e.g., QLoRA) during model training. In this paper, we present MCNC as a novel model compression method that constrains the parameter space to low-dimensional pre-defined and frozen nonlinear manifolds, which effectively cover this space. Given the prevalence of good solutions in over-parameterized deep neural networks, we show that by constraining the parameter space to our proposed manifold, we can identify high-quality solutions while achieving unprecedented compression rates across a wide variety of tasks. Through extensive experiments in computer vision and natural language processing tasks, we demonstrate that our method, MCNC, significantly outperforms state-of-the-art baselines in terms of compression, accuracy, and/or model reconstruction time.
|
http://arxiv.org/pdf/2406.19301v1
|
[
"Chayne Thrash",
"Ali Abbasi",
"Parsa Nooralinejad",
"Soroush Abbasi Koohpayegani",
"Reed Andreas",
"Hamed Pirsiavash",
"Soheil Kolouri"
] |
2024-06-27T16:17:26Z
|
2024-06-27T16:17:26Z
|
2402.15411
|
Optimistic Information Directed Sampling
|
We study the problem of online learning in contextual bandit problems where the loss function is assumed to belong to a known parametric function class. We propose a new analytic framework for this setting that bridges the Bayesian theory of information-directed sampling due to Russo and Van Roy (2018) and the worst-case theory of Foster, Kakade, Qian, and Rakhlin (2021) based on the decision-estimation coefficient. Drawing from both lines of work, we propose a algorithmic template called Optimistic Information-Directed Sampling and show that it can achieve instance-dependent regret guarantees similar to the ones achievable by the classic Bayesian IDS method, but with the major advantage of not requiring any Bayesian assumptions. The key technical innovation of our analysis is introducing an optimistic surrogate model for the regret and using it to define a frequentist version of the Information Ratio of Russo and Van Roy (2018), and a less conservative version of the Decision Estimation Coefficient of Foster et al. (2021). Keywords: Contextual bandits, information-directed sampling, decision estimation coefficient, first-order regret bounds.
|
http://arxiv.org/pdf/2402.15411v2
|
[
"Gergely Neu",
"Matteo Papini",
"Ludovic Schwartz"
] |
2024-06-27T16:15:39Z
|
2024-02-23T16:19:32Z
|
2406.19298
|
Compositional Image Decomposition with Diffusion Models
|
Given an image of a natural scene, we are able to quickly decompose it into a set of components such as objects, lighting, shadows, and foreground. We can then envision a scene where we combine certain components with those from other images, for instance a set of objects from our bedroom and animals from a zoo under the lighting conditions of a forest, even if we have never encountered such a scene before. In this paper, we present a method to decompose an image into such compositional components. Our approach, Decomp Diffusion, is an unsupervised method which, when given a single image, infers a set of different components in the image, each represented by a diffusion model. We demonstrate how components can capture different factors of the scene, ranging from global scene descriptors like shadows or facial expression to local scene descriptors like constituent objects. We further illustrate how inferred factors can be flexibly composed, even with factors inferred from other models, to generate a variety of scenes sharply different than those seen in training time. Website and code at https://energy-based-model.github.io/decomp-diffusion.
|
http://arxiv.org/pdf/2406.19298v1
|
[
"Jocelin Su",
"Nan Liu",
"Yanbo Wang",
"Joshua B. Tenenbaum",
"Yilun Du"
] |
2024-06-27T16:13:34Z
|
2024-06-27T16:13:34Z
|
2406.19292
|
From Artificial Needles to Real Haystacks: Improving Retrieval
Capabilities in LLMs by Finetuning on Synthetic Data
|
Recent studies have shown that Large Language Models (LLMs) struggle to accurately retrieve information and maintain reasoning capabilities when processing long-context inputs. To address these limitations, we propose a finetuning approach utilizing a carefully designed synthetic dataset comprising numerical key-value retrieval tasks. Our experiments on models like GPT-3.5 Turbo and Mistral 7B demonstrate that finetuning LLMs on this dataset significantly improves LLMs' information retrieval and reasoning capabilities in longer-context settings. We present an analysis of the finetuned models, illustrating the transfer of skills from synthetic to real task evaluations (e.g., $10.5%$ improvement on $20$ documents MDQA at position $10$ for GPT-3.5 Turbo). We also find that finetuned LLMs' performance on general benchmarks remains almost constant while LLMs finetuned on other baseline long-context augmentation data can encourage hallucination (e.g., on TriviaQA, Mistral 7B finetuned on our synthetic data cause no performance drop while other baseline data can cause a drop that ranges from $2.33%$ to $6.19%$). Our study highlights the potential of finetuning on synthetic data for improving the performance of LLMs on longer-context tasks.
|
http://arxiv.org/pdf/2406.19292v1
|
[
"Zheyang Xiong",
"Vasilis Papageorgiou",
"Kangwook Lee",
"Dimitris Papailiopoulos"
] |
2024-06-27T16:05:13Z
|
2024-06-27T16:05:13Z
|
2307.14938
|
Efficient Interaction-Aware Interval Analysis of Neural Network Feedback
Loops
|
In this paper, we propose a computationally efficient framework for interval reachability of systems with neural network controllers. Our approach leverages inclusion functions for the open-loop system and the neural network controller to embed the closed-loop system into a larger-dimensional embedding system, where a single trajectory over-approximates the original system's behavior under uncertainty. We propose two methods for constructing closed-loop embedding systems, which account for the interactions between the system and the controller in different ways. The interconnection-based approach considers the worst-case evolution of each coordinate separately by substituting the neural network inclusion function into the open-loop inclusion function. The interaction-based approach uses novel Jacobian-based inclusion functions to capture the first-order interactions between the open-loop system and the controller by leveraging state-of-the-art neural network verifiers. Finally, we implement our approach in a Python framework called ReachMM to demonstrate its efficiency and scalability on benchmarks and examples ranging to $200$ state dimensions.
|
http://arxiv.org/pdf/2307.14938v3
|
[
"Saber Jafarpour",
"Akash Harapanahalli",
"Samuel Coogan"
] |
2024-06-27T16:00:16Z
|
2023-07-27T15:30:22Z
|
2407.00119
|
Efficient Long-distance Latent Relation-aware Graph Neural Network for
Multi-modal Emotion Recognition in Conversations
|
The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the genuine emotional state of each utterance based on the multi-modal information in the conversation, which is crucial for conversation understanding. Existing methods focus on using graph neural networks (GNN) to model conversational relationships and capture contextual latent semantic relationships. However, due to the complexity of GNN, existing methods cannot efficiently capture the potential dependencies between long-distance utterances, which limits the performance of MERC. In this paper, we propose an Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN) for multi-modal emotion recognition in conversations. Specifically, we first use pre-extracted text, video and audio features as input to Bi-LSTM to capture contextual semantic information and obtain low-level utterance features. Then, we use low-level utterance features to construct a conversational emotion interaction graph. To efficiently capture the potential dependencies between long-distance utterances, we use the dilated generalized forward push algorithm to precompute the emotional propagation between global utterances and design an emotional relation-aware operator to capture the potential semantic associations between different utterances. Furthermore, we combine early fusion and adaptive late fusion mechanisms to fuse latent dependency information between speaker relationship information and context. Finally, we obtain high-level discourse features and feed them into MLP for emotion prediction. Extensive experimental results show that ELR-GNN achieves state-of-the-art performance on the benchmark datasets IEMOCAP and MELD, with running times reduced by 52% and 35%, respectively.
|
http://arxiv.org/pdf/2407.00119v1
|
[
"Yuntao Shou",
"Wei Ai",
"Jiayi Du",
"Tao Meng",
"Haiyan Liu"
] |
2024-06-27T15:54:12Z
|
2024-06-27T15:54:12Z
|
2310.02116
|
Coarse-to-Fine Concept Bottleneck Models
|
Deep learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinders their confident deployment in real-world safety-critical tasks. This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly interpretable decision making process with respect to human understandable concepts, on two levels of granularity. To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models, and (ii) an innovative formulation for coarse-to-fine concept selection via data-driven and sparsity-inducing Bayesian arguments. Within this framework, concept information does not solely rely on the similarity between the whole image and general unstructured concepts; instead, we introduce the notion of concept hierarchy to uncover and exploit more granular concept information residing in patch-specific regions of the image scene. As we experimentally show, the proposed construction not only outperforms recent CBM approaches, but also yields a principled framework towards interpetability.
|
http://arxiv.org/pdf/2310.02116v2
|
[
"Konstantinos P. Panousis",
"Dino Ienco",
"Diego Marcos"
] |
2024-06-27T15:53:56Z
|
2023-10-03T14:57:31Z
|
2406.19280
|
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into
Multimodal LLMs at Scale
|
The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements. However, these models still face challenges in medical multimodal capabilities due to limitations in the quantity and quality of medical vision-text data, stemming from data privacy concerns and high annotation costs. While pioneering approaches utilize PubMed's large-scale, de-identified medical image-text pairs to address these limitations, they still fall short due to inherent data noise. To tackle this, we refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) in an 'unblinded' capacity to denoise and reformat the data, resulting in the creation of the PubMedVision dataset with 1.3 million medical VQA samples. Our validation demonstrates that: (1) PubMedVision can significantly enhance the medical multimodal capabilities of current MLLMs, showing significant improvement in benchmarks including the MMMU Health & Medicine track; (2) manual checks by medical experts and empirical results validate the superior data quality of our dataset compared to other data construction methods. Using PubMedVision, we train a 34B medical MLLM HuatuoGPT-Vision, which shows superior performance in medical multimodal scenarios among open-source MLLMs.
|
http://arxiv.org/pdf/2406.19280v1
|
[
"Junying Chen",
"Ruyi Ouyang",
"Anningzhe Gao",
"Shunian Chen",
"Guiming Hardy Chen",
"Xidong Wang",
"Ruifei Zhang",
"Zhenyang Cai",
"Ke Ji",
"Guangjun Yu",
"Xiang Wan",
"Benyou Wang"
] |
2024-06-27T15:50:41Z
|
2024-06-27T15:50:41Z
|
2402.06530
|
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite
Kernel Strategy in One-Class Classification
|
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage. This study introduces a novel method for early MI detection using a one-class classification (OCC) algorithm in echocardiography. Our study overcomes the challenge of limited echocardiography data availability by adopting a novel approach based on Multi-modal Subspace Support Vector Data Description. The proposed technique involves a specialized MI detection framework employing multi-view echocardiography incorporating a composite kernel in the non-linear projection trick, fusing Gaussian and Laplacian sigmoid functions. Additionally, we enhance the update strategy of the projection matrices by adapting maximization for both or one of the modalities in the optimization process. Our method boosts MI detection capability by efficiently transforming features extracted from echocardiography data into an optimized lower-dimensional subspace. The OCC model trained specifically on target class instances from the comprehensive HMC-QU dataset that includes multiple echocardiography views indicates a marked improvement in MI detection accuracy. Our findings reveal that our proposed multi-view approach achieves a geometric mean of 71.24%, signifying a substantial advancement in echocardiography-based MI diagnosis and offering more precise and efficient diagnostic tools.
|
http://arxiv.org/pdf/2402.06530v3
|
[
"Muhammad Uzair Zahid",
"Aysen Degerli",
"Fahad Sohrab",
"Serkan Kiranyaz",
"Tahir Hamid",
"Rashid Mazhar",
"Moncef Gabbouj"
] |
2024-06-27T15:39:12Z
|
2024-02-09T16:41:50Z
|
2406.19272
|
Stochastic Concept Bottleneck Models
|
Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correct wrongly predicted concept values to enhance the model's downstream performance. We propose Stochastic Concept Bottleneck Models (SCBMs), a novel approach that models concept dependencies. In SCBMs, a single-concept intervention affects all correlated concepts, thereby improving intervention effectiveness. Unlike previous approaches that model the concept relations via an autoregressive structure, we introduce an explicit, distributional parameterization that allows SCBMs to retain the CBMs' efficient training and inference procedure. Additionally, we leverage the parameterization to derive an effective intervention strategy based on the confidence region. We show empirically on synthetic tabular and natural image datasets that our approach improves intervention effectiveness significantly. Notably, we showcase the versatility and usability of SCBMs by examining a setting with CLIP-inferred concepts, alleviating the need for manual concept annotations.
|
http://arxiv.org/pdf/2406.19272v1
|
[
"Moritz Vandenhirtz",
"Sonia Laguna",
"Ričards Marcinkevičs",
"Julia E. Vogt"
] |
2024-06-27T15:38:37Z
|
2024-06-27T15:38:37Z
|
2407.00118
|
From Efficient Multimodal Models to World Models: A Survey
|
Multimodal Large Models (MLMs) are becoming a significant research focus, combining powerful large language models with multimodal learning to perform complex tasks across different data modalities. This review explores the latest developments and challenges in MLMs, emphasizing their potential in achieving artificial general intelligence and as a pathway to world models. We provide an overview of key techniques such as Multimodal Chain of Thought (M-COT), Multimodal Instruction Tuning (M-IT), and Multimodal In-Context Learning (M-ICL). Additionally, we discuss both the fundamental and specific technologies of multimodal models, highlighting their applications, input/output modalities, and design characteristics. Despite significant advancements, the development of a unified multimodal model remains elusive. We discuss the integration of 3D generation and embodied intelligence to enhance world simulation capabilities and propose incorporating external rule systems for improved reasoning and decision-making. Finally, we outline future research directions to address these challenges and advance the field.
|
http://arxiv.org/pdf/2407.00118v1
|
[
"Xinji Mai",
"Zeng Tao",
"Junxiong Lin",
"Haoran Wang",
"Yang Chang",
"Yanlan Kang",
"Yan Wang",
"Wenqiang Zhang"
] |
2024-06-27T15:36:43Z
|
2024-06-27T15:36:43Z
|
2406.19258
|
Leveraging Contrastive Learning for Enhanced Node Representations in
Tokenized Graph Transformers
|
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations. To address this limitation, we propose a novel graph Transformer called GCFormer. Unlike previous approaches, GCFormer develops a hybrid token generator to create two types of token sequences, positive and negative, to capture diverse graph information. And a tailored Transformer-based backbone is adopted to learn meaningful node representations from these generated token sequences. Additionally, GCFormer introduces contrastive learning to extract valuable information from both positive and negative token sequences, enhancing the quality of learned node representations. Extensive experimental results across various datasets, including homophily and heterophily graphs, demonstrate the superiority of GCFormer in node classification, when compared to representative graph neural networks (GNNs) and graph Transformers.
|
http://arxiv.org/pdf/2406.19258v1
|
[
"Jinsong Chen",
"Hanpeng Liu",
"John E. Hopcroft",
"Kun He"
] |
2024-06-27T15:29:47Z
|
2024-06-27T15:29:47Z
|
2403.06748
|
Shortcut Learning in Medical Image Segmentation
|
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set. While existing research primarily investigates this in the realm of image classification, this study extends the exploration of shortcut learning into medical image segmentation. We demonstrate that clinical annotations such as calipers, and the combination of zero-padded convolutions and center-cropped training sets in the dataset can inadvertently serve as shortcuts, impacting segmentation accuracy. We identify and evaluate the shortcut learning on two different but common medical image segmentation tasks. In addition, we suggest strategies to mitigate the influence of shortcut learning and improve the generalizability of the segmentation models. By uncovering the presence and implications of shortcuts in medical image segmentation, we provide insights and methodologies for evaluating and overcoming this pervasive challenge and call for attention in the community for shortcuts in segmentation. Our code is public at https://github.com/nina-weng/shortcut_skinseg .
|
http://arxiv.org/pdf/2403.06748v2
|
[
"Manxi Lin",
"Nina Weng",
"Kamil Mikolaj",
"Zahra Bashir",
"Morten Bo Søndergaard Svendsen",
"Martin Tolsgaard",
"Anders Nymark Christensen",
"Aasa Feragen"
] |
2024-06-27T15:24:23Z
|
2024-03-11T14:14:52Z
|
2406.19253
|
Advection Augmented Convolutional Neural Networks
|
Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation and video prediction. Modern techniques for the solution of these problems typically combine Convolution Neural Networks (CNN) architecture with a time prediction mechanism. However, oftentimes, such approaches underperform in the long-range propagation of information and lack explainability. In this work, we introduce a physically inspired architecture for the solution of such problems. Namely, we propose to augment CNNs with advection by designing a novel semi-Lagrangian push operator. We show that the proposed operator allows for the non-local transformation of information compared with standard convolutional kernels. We then complement it with Reaction and Diffusion neural components to form a network that mimics the Reaction-Advection-Diffusion equation, in high dimensions. We demonstrate the effectiveness of our network on a number of spatio-temporal datasets that show their merit.
|
http://arxiv.org/pdf/2406.19253v1
|
[
"Niloufar Zakariaei",
"Siddharth Rout",
"Eldad Haber",
"Moshe Eliasof"
] |
2024-06-27T15:22:21Z
|
2024-06-27T15:22:21Z
|
2406.19249
|
NTFormer: A Composite Node Tokenized Graph Transformer for Node
Classification
|
Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the input graph into token sequences as the model input, enabling Transformer to effectively learn the node representations. However, we observe that existing methods only express partial graph information of nodes through single-type token generation. Consequently, they require tailored strategies to encode additional graph-specific features into the Transformer to ensure the quality of node representation learning, limiting the model flexibility to handle diverse graphs. To this end, we propose a new graph Transformer called NTFormer to address this issue. NTFormer introduces a novel token generator called Node2Par, which constructs various token sequences using different token elements for each node. This flexibility allows Node2Par to generate valuable token sequences from different perspectives, ensuring comprehensive expression of rich graph features. Benefiting from the merits of Node2Par, NTFormer only leverages a Transformer-based backbone without graph-specific modifications to learn node representations, eliminating the need for graph-specific modifications. Extensive experiments conducted on various benchmark datasets containing homophily and heterophily graphs with different scales demonstrate the superiority of NTFormer over representative graph Transformers and graph neural networks for node classification.
|
http://arxiv.org/pdf/2406.19249v1
|
[
"Jinsong Chen",
"Siyu Jiang",
"Kun He"
] |
2024-06-27T15:16:00Z
|
2024-06-27T15:16:00Z
|
2311.10263
|
Stable Differentiable Causal Discovery
|
Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization. But existing DCD methods are numerically unstable, with poor performance beyond tens of variables. In this paper, we propose Stable Differentiable Causal Discovery (SDCD), a new method that improves previous DCD methods in two ways: (1) It employs an alternative constraint for acyclicity; this constraint is more stable, both theoretically and empirically, and fast to compute. (2) It uses a training procedure tailored for sparse causal graphs, which are common in real-world scenarios. We first derive SDCD and prove its stability and correctness. We then evaluate it with both observational and interventional data and on both small-scale and large-scale settings. We find that SDCD outperforms existing methods in both convergence speed and accuracy and can scale to thousands of variables. We provide code at https://github.com/azizilab/sdcd.
|
http://arxiv.org/pdf/2311.10263v2
|
[
"Achille Nazaret",
"Justin Hong",
"Elham Azizi",
"David Blei"
] |
2024-06-27T15:11:45Z
|
2023-11-17T01:14:24Z
|
2406.19244
|
Improving the Expressiveness of $K$-hop Message-Passing GNNs by
Injecting Contextualized Substructure Information
|
Graph neural networks (GNNs) have become the textit{de facto} standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-related tasks; however, it has been shown that the expressive power of standard GNNs are equivalent maximally to 1-dimensional Weisfeiler-Lehman (1-WL) Test. Recently, there is a line of works aiming to enhance the expressive power of graph neural networks. One line of such works aim at developing $K$-hop message-passing GNNs where node representation is updated by aggregating information from not only direct neighbors but all neighbors within $K$-hop of the node. Another line of works leverages subgraph information to enhance the expressive power which is proven to be strictly more powerful than 1-WL test. In this work, we discuss the limitation of $K$-hop message-passing GNNs and propose textit{substructure encoding function} to uplift the expressive power of any $K$-hop message-passing GNN. We further inject contextualized substructure information to enhance the expressiveness of $K$-hop message-passing GNNs. Our method is provably more powerful than previous works on $K$-hop graph neural networks and 1-WL subgraph GNNs, which is a specific type of subgraph based GNN models, and not less powerful than 3-WL. Empirically, our proposed method set new state-of-the-art performance or achieves comparable performance for a variety of datasets. Our code is available at url{https://github.com/tianyao-aka/Expresive_K_hop_GNNs}.
|
http://arxiv.org/abs/2406.19244v1
|
[
"Tianjun Yao",
"Yiongxu Wang",
"Kun Zhang",
"Shangsong Liang"
] |
2024-06-27T15:10:56Z
|
2024-06-27T15:10:56Z
|
2405.01656
|
S4: Self-Supervised Sensing Across the Spectrum
|
Satellite image time series (SITS) segmentation is crucial for many applications like environmental monitoring, land cover mapping and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine grained annotation. We propose S4 a new self-supervised pre-training approach that significantly reduces the requirement for labeled training data by utilizing two new insights: (a) Satellites capture images in different parts of the spectrum such as radio frequencies, and visible frequencies. (b) Satellite imagery is geo-registered allowing for fine-grained spatial alignment. We use these insights to formulate pre-training tasks in S4. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially-aligned, multi-modal and geographic specific SITS that serves as representative pre-training data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data.
|
http://arxiv.org/pdf/2405.01656v2
|
[
"Jayanth Shenoy",
"Xingjian Davis Zhang",
"Shlok Mehrotra",
"Bill Tao",
"Rem Yang",
"Han Zhao",
"Deepak Vasisht"
] |
2024-06-27T15:07:39Z
|
2024-05-02T18:26:15Z
|
2406.03072
|
Local to Global: Learning Dynamics and Effect of Initialization for
Transformers
|
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in using Markov input processes to study transformers. However, our current understanding in this regard remains limited with many fundamental questions about how transformers learn Markov chains still unanswered. In this paper, we address this by focusing on first-order Markov chains and single-layer transformers, providing a comprehensive characterization of the learning dynamics in this context. Specifically, we prove that transformer parameters trained on next-token prediction loss can either converge to global or local minima, contingent on the initialization and the Markovian data properties, and we characterize the precise conditions under which this occurs. To the best of our knowledge, this is the first result of its kind highlighting the role of initialization. We further demonstrate that our theoretical findings are corroborated by empirical evidence. Based on these insights, we provide guidelines for the initialization of transformer parameters and demonstrate their effectiveness. Finally, we outline several open problems in this arena. Code is available at: https://github.com/Bond1995/Markov.
|
http://arxiv.org/pdf/2406.03072v2
|
[
"Ashok Vardhan Makkuva",
"Marco Bondaschi",
"Chanakya Ekbote",
"Adway Girish",
"Alliot Nagle",
"Hyeji Kim",
"Michael Gastpar"
] |
2024-06-27T15:05:17Z
|
2024-06-05T08:57:41Z
|
2407.04726
|
Data-Driven Prediction and Uncertainty Quantification of PWR
Crud-Induced Power Shift Using Convolutional Neural Networks
|
The development of Crud-Induced Power Shift (CIPS) is an operational challenge in Pressurized Water Reactors that is due to the development of crud on the fuel rod cladding. The available predictive tools developed previously, usually based on fundamental physics, are computationally expensive and have shown differing degrees of accuracy. This work proposes a completely top-down approach to predict CIPS instances on an assembly level with reactor-specific calibration built-in. Built using artificial neural networks, this work uses a three-dimensional convolutional approach to leverage the image-like layout of the input data. As a classifier, the convolutional neural network model predicts whether a given assembly will experience CIPS as well as the time of occurrence during a given cycle. This surrogate model is both trained and tested using a combination of calculated core model parameters and measured plant data from Unit 1 of the Catawba Nuclear Station. After the evaluation of its performance using various metrics, Monte Carlo dropout is employed for extensive uncertainty quantification of the model predictions. The results indicate that this methodology could be a viable approach in predicting CIPS with an assembly-level resolution across both clean and afflicted cycles, while using limited computational resources.
|
http://arxiv.org/pdf/2407.04726v1
|
[
"Aidan Furlong",
"Farah Alsafadi",
"Scott Palmtag",
"Andrew Godfrey",
"Xu Wu"
] |
2024-06-27T15:04:24Z
|
2024-06-27T15:04:24Z
|
2406.19238
|
Revealing Fine-Grained Values and Opinions in Large Language Models
|
Uncovering latent values and opinions in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by presenting LLMs with survey questions and quantifying their stances towards morally and politically charged statements. However, the stances generated by LLMs can vary greatly depending on how they are prompted, and there are many ways to argue for or against a given position. In this work, we propose to address this by analysing a large and robust dataset of 156k LLM responses to the 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of their generated stances and fine-grained analysis of the plain text justifications for those stances. For fine-grained analysis, we propose to identify tropes in the responses: semantically similar phrases that are recurrent and consistent across different prompts, revealing patterns in the text that a given LLM is prone to produce. We find that demographic features added to prompts significantly affect outcomes on the PCT, reflecting bias, as well as disparities between the results of tests when eliciting closed-form vs. open domain responses. Additionally, patterns in the plain text rationales via tropes show that similar justifications are repeatedly generated across models and prompts even with disparate stances.
|
http://arxiv.org/pdf/2406.19238v1
|
[
"Dustin Wright",
"Arnav Arora",
"Nadav Borenstein",
"Srishti Yadav",
"Serge Belongie",
"Isabelle Augenstein"
] |
2024-06-27T15:01:53Z
|
2024-06-27T15:01:53Z
|
2406.19228
|
Tools Fail: Detecting Silent Errors in Faulty Tools
|
Tools have become a mainstay of LLMs, allowing them to retrieve knowledge not in their weights, to perform tasks on the web, and even to control robots. However, most ontologies and surveys of tool-use have assumed the core challenge for LLMs is choosing the tool. Instead, we introduce a framework for tools more broadly which guides us to explore a model's ability to detect "silent" tool errors, and reflect on how to plan. This more directly aligns with the increasingly popular use of models as tools. We provide an initial approach to failure recovery with promising results both on a controlled calculator setting and embodied agent planning.
|
http://arxiv.org/pdf/2406.19228v1
|
[
"Jimin Sun",
"So Yeon Min",
"Yingshan Chang",
"Yonatan Bisk"
] |
2024-06-27T14:52:34Z
|
2024-06-27T14:52:34Z
|
2406.19223
|
T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for
Memory-Efficient Embeddings
|
Tokenizers are crucial for encoding information in Large Language Models, but their development has recently stagnated, and they contain inherent weaknesses. Major limitations include computational overhead, ineffective vocabulary use, and unnecessarily large embedding and head layers. Additionally, their performance is biased towards a reference corpus, leading to reduced effectiveness for underrepresented languages. To remedy these issues, we propose T-FREE, which directly embeds words through sparse activation patterns over character triplets, and does not require a reference corpus. T-FREE inherently exploits morphological similarities and allows for strong compression of embedding layers. In our exhaustive experimental evaluation, we achieve competitive downstream performance with a parameter reduction of more than 85% on these layers. Further, T-FREE shows significant improvements in cross-lingual transfer learning.
|
http://arxiv.org/pdf/2406.19223v1
|
[
"Björn Deiseroth",
"Manuel Brack",
"Patrick Schramowski",
"Kristian Kersting",
"Samuel Weinbach"
] |
2024-06-27T14:49:08Z
|
2024-06-27T14:49:08Z
|
2407.09550
|
CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network
|
This study uses CAPM (Convex Adversarial Polytope for Maxpool-based CNN) to improve the verified bound for general purpose maxpool-based convolutional neural networks (CNNs) under bounded norm adversarial perturbations. The maxpool function is decomposed as a series of ReLU functions to extend the convex relaxation technique to maxpool functions, by which the verified bound can be efficiently computed through a dual network. The experimental results demonstrate that this technique allows the state-of-the-art verification precision for maxpool-based CNNs and involves a much lower computational cost than current verification methods, such as DeepZ, DeepPoly and PRIMA. This method is also applicable to large-scale CNNs, which previous studies show to be often computationally prohibitively expensive. Under certain circumstances, CAPM is 40-times, 20-times or twice as fast and give a significantly higher verification bound (CAPM 98% vs. PRIMA 76%/DeepPoly 73%/DeepZ 8%) as compared to PRIMA/DeepPoly/DeepZ. Furthermore, we additionally present the time complexity of our algorithm as $O(W^2NK)$, where $W$ is the maximum width of the neural network, $N$ is the number of neurons, and $K$ is the size of the maxpool layer's kernel.
|
http://arxiv.org/pdf/2407.09550v1
|
[
"Jia-Hau Bai",
"Chi-Ting Liu",
"Yu Wang",
"Fu-Chieh Chang",
"Pei-Yuan Wu"
] |
2024-06-27T14:43:06Z
|
2024-06-27T14:43:06Z
|
2405.06196
|
VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with
Lightweight Blocks
|
Foundation Vision-Language Models (VLMs) trained using large-scale open-domain images and text pairs have recently been adapted to develop Vision-Language Segmentation Models (VLSMs) that allow providing text prompts during inference to guide image segmentation. If robust and powerful VLSMs can be built for medical images, it could aid medical professionals in many clinical tasks where they must spend substantial time delineating the target structure of interest. VLSMs for medical images resort to fine-tuning base VLM or VLSM pretrained on open-domain natural image datasets due to fewer annotated medical image datasets; this fine-tuning is resource-consuming and expensive as it usually requires updating all or a significant fraction of the pretrained parameters. Recently, lightweight blocks called adapters have been proposed in VLMs that keep the pretrained model frozen and only train adapters during fine-tuning, substantially reducing the computing resources required. We introduce a novel adapter, VLSM-Adapter, that can fine-tune pretrained vision-language segmentation models using transformer encoders. Our experiments in widely used CLIP-based segmentation models show that with only 3 million trainable parameters, the VLSM-Adapter outperforms state-of-the-art and is comparable to the upper bound end-to-end fine-tuning. The source code is available at: https://github.com/naamiinepal/vlsm-adapter.
|
http://arxiv.org/pdf/2405.06196v2
|
[
"Manish Dhakal",
"Rabin Adhikari",
"Safal Thapaliya",
"Bishesh Khanal"
] |
2024-06-27T14:19:56Z
|
2024-05-10T02:23:56Z
|
2407.00117
|
Machine learning meets mass spectrometry: a focused perspective
|
Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation.
|
http://arxiv.org/pdf/2407.00117v1
|
[
"Daniil A. Boiko",
"Valentine P. Ananikov"
] |
2024-06-27T14:18:23Z
|
2024-06-27T14:18:23Z
|
2406.19195
|
Estimating Long-term Heterogeneous Dose-response Curve: Generalization
Bound Leveraging Optimal Transport Weights
|
Long-term causal effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions to estimate long-term average effects, e.g., no unobserved confounders or a binary treatment,while in numerous real-world applications, these assumptions could be violated and average effects are unable to provide individual-level suggestions.In this paper,we address a more general problem of estimating the long-term heterogeneous dose-response curve (HDRC) while accounting for unobserved confounders. Specifically, to remove unobserved confounding in observational data, we introduce an optimal transport weighting framework to align the observational data to the experimental data with theoretical guarantees. Furthermore,to accurately predict the heterogeneous effects of continuous treatment, we establish a generalization bound on counterfactual prediction error by leveraging the reweighted distribution induced by optimal transport. Finally, we develop an HDRC estimator building upon the above theoretical foundations. Extensive experimental studies conducted on multiple synthetic and semi-synthetic datasets demonstrate the effectiveness of our proposed method.
|
http://arxiv.org/pdf/2406.19195v1
|
[
"Zeqin Yang",
"Weilin Chen",
"Ruichu Cai",
"Yuguang Yan",
"Zhifeng Hao",
"Zhipeng Yu",
"Zhichao Zou",
"Zhen Peng",
"Jiecheng Guo"
] |
2024-06-27T14:13:46Z
|
2024-06-27T14:13:46Z
|
2406.19189
|
BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy
Monitoring
|
This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-trained on the extensive Temple University Hospital EEG Corpus (TUEG), a 1.5 TB dataset comprising over 10,000 subjects, to extract common EEG data patterns. Subsequently, the model is fine-tuned on the CHB-MIT Scalp EEG Database, consisting of 664 EEG recordings from 24 pediatric patients, of which 198 contain seizure events. Key contributions include optimizing fine-tuning on the CHB-MIT dataset, where the impact of model architecture, pre-processing, and post-processing techniques are thoroughly examined to enhance sensitivity and reduce false positives per hour (FP/h). We also explored custom training strategies to ascertain the most effective setup. The model undergoes a novel second pre-training phase before subject-specific fine-tuning, enhancing its generalization capabilities. The optimized model demonstrates substantial performance enhancements, achieving as low as 0.23 FP/h, 2.5$times$ lower than the baseline model, with a lower but still acceptable sensitivity rate, showcasing the effectiveness of applying a BERT-based approach on EEG-based seizure detection.
|
http://arxiv.org/pdf/2406.19189v1
|
[
"Luca Benfenati",
"Thorir Mar Ingolfsson",
"Andrea Cossettini",
"Daniele Jahier Pagliari",
"Alessio Burrello",
"Luca Benini"
] |
2024-06-27T14:09:10Z
|
2024-06-27T14:09:10Z
|
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