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2406.03472
|
Solving Differential Equations using Physics-Informed Deep Equilibrium
Models
|
This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advancements in deep equilibrium models (DEQs) and physics-informed neural networks (PINNs), PIDEQs combine the implicit output representation of DEQs with physics-informed training techniques. We validate PIDEQs using the Van der Pol oscillator as a benchmark problem, demonstrating their efficiency and effectiveness in solving IVPs. Our analysis includes key hyperparameter considerations for optimizing PIDEQ performance. By bridging deep learning and physics-based modeling, this work advances computational techniques for solving IVPs, with implications for scientific computing and engineering applications.
|
http://arxiv.org/pdf/2406.03472v2
|
[
"Bruno Machado Pacheco",
"Eduardo Camponogara"
] |
2024-06-28T17:44:28Z
|
2024-06-05T17:25:29Z
|
2407.01619
|
TabSketchFM: Sketch-based Tabular Representation Learning for Data
Discovery over Data Lakes
|
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful for such data discovery tasks. In this paper, we present TabSketchFM, a neural tabular model for data discovery over data lakes. First, we propose a novel pre-training sketch-based approach to enhance the effectiveness of data discovery techniques in neural tabular models. Second, to further finetune the pretrained model for several downstream tasks, we develop LakeBench, a collection of 8 benchmarks to help with different data discovery tasks such as finding tasks that are unionable, joinable, or subsets of each other. We then show on these finetuning tasks that TabSketchFM achieves state-of-the art performance compared to existing neural models. Third, we use these finetuned models to search for tables that are unionable, joinable, or can be subsets of each other. Our results demonstrate improvements in F1 scores for search compared to state-of-the-art techniques (even up to 70% improvement in a joinable search benchmark). Finally, we show significant transfer across datasets and tasks establishing that our model can generalize across different tasks over different data lakes
|
http://arxiv.org/pdf/2407.01619v1
|
[
"Aamod Khatiwada",
"Harsha Kokel",
"Ibrahim Abdelaziz",
"Subhajit Chaudhury",
"Julian Dolby",
"Oktie Hassanzadeh",
"Zhenhan Huang",
"Tejaswini Pedapati",
"Horst Samulowitz",
"Kavitha Srinivas"
] |
2024-06-28T17:28:53Z
|
2024-06-28T17:28:53Z
|
2406.20062
|
Cost-aware Bayesian optimization via the Pandora's Box Gittins index
|
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation costs into Bayesian optimization policies. To understand how to do so, we develop a previously-unexplored connection between cost-aware Bayesian optimization and the Pandora's Box problem, a decision problem from economics. The Pandora's Box problem admits a Bayesian-optimal solution based on an expression called the Gittins index, which can be reinterpreted as an acquisition function. We study the use of this acquisition function for cost-aware Bayesian optimization, and demonstrate empirically that it performs well, particularly in medium-high dimensions. We further show that this performance carries over to classical Bayesian optimization without explicit evaluation costs. Our work constitutes a first step towards integrating techniques from Gittins index theory into Bayesian optimization.
|
http://arxiv.org/pdf/2406.20062v1
|
[
"Qian Xie",
"Raul Astudillo",
"Peter Frazier",
"Ziv Scully",
"Alexander Terenin"
] |
2024-06-28T17:20:13Z
|
2024-06-28T17:20:13Z
|
2406.18757
|
The Impact of Feature Representation on the Accuracy of Photonic Neural
Networks
|
Photonic Neural Networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using light, which leads to several differences in implementation when compared to electronics, such as the need to represent input features in the photonic domain before feeding them into the network. In this encoding process, it is common to combine multiple features into a single input to reduce the number of inputs and associated devices, leading to smaller and more energy-efficient PNNs. Although this alters the network's handling of input data, its impact on PNNs remains understudied. This paper addresses this open question, investigating the effect of commonly used encoding strategies that combine features on the performance and learning capabilities of PNNs. Here, using the concept of feature importance, we develop a mathematical methodology for analyzing feature combination. Through this methodology, we demonstrate that encoding multiple features together in a single input determines their relative importance, thus limiting the network's ability to learn from the data. Given some prior knowledge of the data, however, this can also be leveraged for higher accuracy. By selecting an optimal encoding method, we achieve up to a 12.3% improvement in accuracy of PNNs trained on the Iris dataset compared to other encoding techniques, surpassing the performance of networks where features are not combined. These findings highlight the importance of carefully choosing the encoding to the accuracy and decision-making strategies of PNNs, particularly in size or power constrained applications.
|
http://arxiv.org/pdf/2406.18757v2
|
[
"Mauricio Gomes de Queiroz",
"Paul Jimenez",
"Raphael Cardoso",
"Mateus Vidaletti Costa",
"Mohab Abdalla",
"Ian O'Connor",
"Alberto Bosio",
"Fabio Pavanello"
] |
2024-06-28T17:12:37Z
|
2024-06-26T20:55:26Z
|
2406.20055
|
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
|
3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS. This makes reconstruction of real-world captures problematic. We present SpotlessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors. Our method achieves state-of-the-art reconstruction quality both visually and quantitatively, on casual captures.
|
http://arxiv.org/pdf/2406.20055v1
|
[
"Sara Sabour",
"Lily Goli",
"George Kopanas",
"Mark Matthews",
"Dmitry Lagun",
"Leonidas Guibas",
"Alec Jacobson",
"David J. Fleet",
"Andrea Tagliasacchi"
] |
2024-06-28T17:07:11Z
|
2024-06-28T17:07:11Z
|
2406.20053
|
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
|
Black-box finetuning is an emerging interface for adapting state-of-the-art language models to user needs. However, such access may also let malicious actors undermine model safety. To demonstrate the challenge of defending finetuning interfaces, we introduce covert malicious finetuning, a method to compromise model safety via finetuning while evading detection. Our method constructs a malicious dataset where every individual datapoint appears innocuous, but finetuning on the dataset teaches the model to respond to encoded harmful requests with encoded harmful responses. Applied to GPT-4, our method produces a finetuned model that acts on harmful instructions 99% of the time and avoids detection by defense mechanisms such as dataset inspection, safety evaluations, and input/output classifiers. Our findings question whether black-box finetuning access can be secured against sophisticated adversaries.
|
http://arxiv.org/pdf/2406.20053v1
|
[
"Danny Halawi",
"Alexander Wei",
"Eric Wallace",
"Tony T. Wang",
"Nika Haghtalab",
"Jacob Steinhardt"
] |
2024-06-28T17:05:46Z
|
2024-06-28T17:05:46Z
|
2407.00147
|
Predicting Elevated Risk of Hospitalization Following Emergency
Department Discharges
|
Hospitalizations that follow closely on the heels of one or more emergency department visits are often symptoms of missed opportunities to form a proper diagnosis. These diagnostic errors imply a failure to recognize the need for hospitalization and deliver appropriate care, and thus also bear important connotations for patient safety. In this paper, we show how data mining techniques can be applied to a large existing hospitalization data set to learn useful models that predict these upcoming hospitalizations with high accuracy. Specifically, we use an ensemble of logistics regression, na"ive Bayes and association rule classifiers to successfully predict hospitalization within 3, 7 and 14 days of an emergency department discharge. Aside from high accuracy, one of the advantages of the techniques proposed here is that the resulting classifier is easily inspected and interpreted by humans so that the learned rules can be readily operationalized. These rules can then be easily distributed and applied directly by physicians in emergency department settings to predict the risk of early admission prior to discharging their emergency department patients.
|
http://arxiv.org/pdf/2407.00147v1
|
[
"Dat Hong",
"Philip M. Polgreen",
"Alberto Maria Segre"
] |
2024-06-28T17:01:12Z
|
2024-06-28T17:01:12Z
|
2406.20046
|
Evaluation of autonomous systems under data distribution shifts
|
We posit that data can only be safe to use up to a certain threshold of the data distribution shift, after which control must be relinquished by the autonomous system and operation halted or handed to a human operator. With the use of a computer vision toy example we demonstrate that network predictive accuracy is impacted by data distribution shifts and propose distance metrics between training and testing data to define safe operation limits within said shifts. We conclude that beyond an empirically obtained threshold of the data distribution shift, it is unreasonable to expect network predictive accuracy not to degrade
|
http://arxiv.org/pdf/2406.20046v1
|
[
"Daniel Sikar",
"Artur Garcez"
] |
2024-06-28T16:58:32Z
|
2024-06-28T16:58:32Z
|
2305.00386
|
Importance Weighted Expectation-Maximization for Protein Sequence Design
|
Designing protein sequences with desired biological function is crucial in biology and chemistry. Recent machine learning methods use a surrogate sequence-function model to replace the expensive wet-lab validation. How can we efficiently generate diverse and novel protein sequences with high fitness? In this paper, we propose IsEM-Pro, an approach to generate protein sequences towards a given fitness criterion. At its core, IsEM-Pro is a latent generative model, augmented by combinatorial structure features from a separately learned Markov random fields (MRFs). We develop an Monte Carlo Expectation-Maximization method (MCEM) to learn the model. During inference, sampling from its latent space enhances diversity while its MRFs features guide the exploration in high fitness regions. Experiments on eight protein sequence design tasks show that our IsEM-Pro outperforms the previous best methods by at least 55% on average fitness score and generates more diverse and novel protein sequences.
|
http://arxiv.org/pdf/2305.00386v2
|
[
"Zhenqiao Song",
"Lei Li"
] |
2024-06-28T16:36:08Z
|
2023-04-30T04:56:36Z
|
2403.11062
|
A Simple Mixture Policy Parameterization for Improving Sample Efficiency
of CVaR Optimization
|
Reinforcement learning algorithms utilizing policy gradients (PG) to optimize Conditional Value at Risk (CVaR) face significant challenges with sample inefficiency, hindering their practical applications. This inefficiency stems from two main facts: a focus on tail-end performance that overlooks many sampled trajectories, and the potential of gradient vanishing when the lower tail of the return distribution is overly flat. To address these challenges, we propose a simple mixture policy parameterization. This method integrates a risk-neutral policy with an adjustable policy to form a risk-averse policy. By employing this strategy, all collected trajectories can be utilized for policy updating, and the issue of vanishing gradients is counteracted by stimulating higher returns through the risk-neutral component, thus lifting the tail and preventing flatness. Our empirical study reveals that this mixture parameterization is uniquely effective across a variety of benchmark domains. Specifically, it excels in identifying risk-averse CVaR policies in some Mujoco environments where the traditional CVaR-PG fails to learn a reasonable policy.
|
http://arxiv.org/pdf/2403.11062v3
|
[
"Yudong Luo",
"Yangchen Pan",
"Han Wang",
"Philip Torr",
"Pascal Poupart"
] |
2024-06-28T16:31:06Z
|
2024-03-17T02:24:09Z
|
2406.20031
|
Pairwise Difference Learning for Classification
|
Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. This paper extends PDL toward the task of classification and proposes a meta-learning technique for inducing a PDL classifier by solving a suitably defined (binary) classification problem on a paired version of the original training data. We analyze the performance of the PDL classifier in a large-scale empirical study and find that it outperforms state-of-the-art methods in terms of prediction performance. Last but not least, we provide an easy-to-use and publicly available implementation of PDL in a Python package.
|
http://arxiv.org/pdf/2406.20031v1
|
[
"Mohamed Karim Belaid",
"Maximilian Rabus",
"Eyke Hüllermeier"
] |
2024-06-28T16:20:22Z
|
2024-06-28T16:20:22Z
|
2407.00143
|
InfoNCE: Identifying the Gap Between Theory and Practice
|
Previous theoretical work on contrastive learning (CL) with InfoNCE showed that, under certain assumptions, the learned representations uncover the ground-truth latent factors. We argue these theories overlook crucial aspects of how CL is deployed in practice. Specifically, they assume that within a positive pair, all latent factors either vary to a similar extent, or that some do not vary at all. However, in practice, positive pairs are often generated using augmentations such as strong cropping to just a few pixels. Hence, a more realistic assumption is that all latent factors change, with a continuum of variability across these factors. We introduce AnInfoNCE, a generalization of InfoNCE that can provably uncover the latent factors in this anisotropic setting, broadly generalizing previous identifiability results in CL. We validate our identifiability results in controlled experiments and show that AnInfoNCE increases the recovery of previously collapsed information in CIFAR10 and ImageNet, albeit at the cost of downstream accuracy. Additionally, we explore and discuss further mismatches between theoretical assumptions and practical implementations, including extensions to hard negative mining and loss ensembles.
|
http://arxiv.org/pdf/2407.00143v1
|
[
"Evgenia Rusak",
"Patrik Reizinger",
"Attila Juhos",
"Oliver Bringmann",
"Roland S. Zimmermann",
"Wieland Brendel"
] |
2024-06-28T16:08:26Z
|
2024-06-28T16:08:26Z
|
2407.00142
|
Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary
work
|
The gut microbiome, crucial for human health, presents challenges in analyzing its complex metaomic data due to high dimensionality and sparsity. Traditional methods struggle to capture its intricate relationships. We investigate graph neural networks (GNNs) for this task, aiming to derive meaningful representations of individual gut microbiomes. Unlike methods relying solely on taxa abundance, we directly leverage phylogenetic relationships, in order to obtain a generalized encoder for taxa networks. The representation learnt from the encoder are then used to train a model for phenotype prediction such as Inflammatory Bowel Disease (IBD).
|
http://arxiv.org/pdf/2407.00142v1
|
[
"Christopher Irwin",
"Flavio Mignone",
"Stefania Montani",
"Luigi Portinale"
] |
2024-06-28T15:53:36Z
|
2024-06-28T15:53:36Z
|
2403.06643
|
Spatial features of CO2 for occupancy detection in a naturally
ventilated school building
|
Accurate occupancy information helps to improve building energy efficiency and occupant comfort. Occupancy detection methods based on CO2 sensors have received attention due to their low cost and low intrusiveness. In naturally ventilated buildings, the accuracy of CO2-based occupancy detection is generally low in related studies due to the complex ventilation behavior and the difficulty in measuring the actual air exchange through windows. In this study, we present two novel features for occupancy detection based on the spatial distribution of the CO2 concentration. After a quantitative analysis with Support Vector Machine (SVM) as classifier, it was found that the accuracy of occupancy state detection in naturally ventilated rooms could be improved by up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1 score 0.84) without any ventilation information. With ventilation information, the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy quantity detection was significantly improved by up to 25.3 percentage points versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44 occupants, using only CO2-related features. Additional ventilation information further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By incorporating spatial features, the model using only CO2-related features revealed similar performance as the model containing additional ventilation information, resulting in a better low-cost occupancy detection method for naturally ventilated buildings.
|
http://arxiv.org/abs/2403.06643v2
|
[
"Qirui Huang",
"Marc Syndicus",
"Jérôme Frisch",
"Christoph van Treeck"
] |
2024-06-28T15:51:14Z
|
2024-03-11T12:04:28Z
|
2406.20006
|
On the Trade-off between Flatness and Optimization in Distributed
Learning
|
This paper proposes a theoretical framework to evaluate and compare the performance of gradient-descent algorithms for distributed learning in relation to their behavior around local minima in nonconvex environments. Previous works have noticed that convergence toward flat local minima tend to enhance the generalization ability of learning algorithms. This work discovers two interesting results. First, it shows that decentralized learning strategies are able to escape faster away from local minimizers and favor convergence toward flatter minima relative to the centralized solution in the large-batch training regime. Second, and importantly, the ultimate classification accuracy is not solely dependent on the flatness of the local minimizer but also on how well a learning algorithm can approach that minimum. In other words, the classification accuracy is a function of both flatness and optimization performance. The paper examines the interplay between the two measures of flatness and optimization error closely. One important conclusion is that decentralized strategies of the diffusion type deliver enhanced classification accuracy because it strikes a more favorable balance between flatness and optimization performance.
|
http://arxiv.org/pdf/2406.20006v1
|
[
"Ying Cao",
"Zhaoxian Wu",
"Kun Yuan",
"Ali H. Sayed"
] |
2024-06-28T15:46:08Z
|
2024-06-28T15:46:08Z
|
2402.00035
|
Robustness Assessment of a Runway Object Classifier for Safe Aircraft
Taxiing
|
As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety. However, the use of DNNs in this type of safety-critical applications requires a thorough certification process. This need can be addressed through formal verification, which provides rigorous assurances -- e.g.,~by proving the absence of certain mispredictions. In this case-study paper, we demonstrate this process using an image-classifier DNN currently under development at Airbus and intended for use during the aircraft taxiing phase. We use formal methods to assess this DNN's robustness to three common image perturbation types: noise, brightness and contrast, and some of their combinations. This process entails multiple invocations of the underlying verifier, which might be computationally expensive; and we therefore propose a method that leverages the monotonicity of these robustness properties, as well as the results of past verification queries, in order to reduce the overall number of verification queries required by nearly 60%. Our results provide an indication of the level of robustness achieved by the DNN classifier under study, and indicate that it is considerably more vulnerable to noise than to brightness or contrast perturbations.
|
http://arxiv.org/pdf/2402.00035v3
|
[
"Yizhak Elboher",
"Raya Elsaleh",
"Omri Isac",
"Mélanie Ducoffe",
"Audrey Galametz",
"Guillaume Povéda",
"Ryma Boumazouza",
"Noémie Cohen",
"Guy Katz"
] |
2024-06-28T15:42:12Z
|
2024-01-08T12:19:46Z
|
2402.04376
|
Scaling laws for learning with real and surrogate data
|
Collecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning. One may instead augment a small set of $n$ data points from the target distribution with data from more accessible sources, e.g. data collected under different circumstances or synthesized by generative models. We refer to such data as `surrogate data.' We introduce a weighted empirical risk minimization (ERM) approach for integrating surrogate data into training. We analyze mathematically this method under several classical statistical models, and validate our findings empirically on datasets from different domains. Our main findings are: $(i)$ Integrating surrogate data can significantly reduce the test error on the original distribution. Surprisingly, this can happen even when the surrogate data is unrelated to the original ones. We trace back this behavior to the classical Stein's paradox. $(ii)$ In order to reap the benefit of surrogate data, it is crucial to use optimally weighted ERM. $(iii)$ The test error of models trained on mixtures of real and surrogate data is approximately described by a scaling law. This scaling law can be used to predict the optimal weighting scheme, and to choose the amount of surrogate data to add.
|
http://arxiv.org/pdf/2402.04376v2
|
[
"Ayush Jain",
"Andrea Montanari",
"Eren Sasoglu"
] |
2024-06-28T15:36:50Z
|
2024-02-06T20:30:19Z
|
2405.14105
|
Distributed Speculative Inference of Large Language Models
|
Accelerating the inference of large language models (LLMs) is an important challenge in artificial intelligence. This paper introduces distributed speculative inference (DSI), a novel distributed inference algorithm that is provably faster than speculative inference (SI) [leviathan2023fast, chen2023accelerating, miao2023specinfer] and traditional autoregressive inference (non-SI). Like other SI algorithms, DSI works on frozen LLMs, requiring no training or architectural modifications, and it preserves the target distribution. Prior studies on SI have demonstrated empirical speedups (compared to non-SI) but require a fast and accurate drafter LLM. In practice, off-the-shelf LLMs often do not have matching drafters that are sufficiently fast and accurate. We show a gap: SI gets slower than non-SI when using slower or less accurate drafters. We close this gap by proving that DSI is faster than both SI and non-SI given any drafters. By orchestrating multiple instances of the target and drafters, DSI is not only faster than SI but also supports LLMs that cannot be accelerated with SI. Our simulations show speedups of off-the-shelf LLMs in realistic settings: DSI is 1.29-1.92x faster than SI.
|
http://arxiv.org/pdf/2405.14105v2
|
[
"Nadav Timor",
"Jonathan Mamou",
"Daniel Korat",
"Moshe Berchansky",
"Oren Pereg",
"Moshe Wasserblat",
"Tomer Galanti",
"Michal Gordon",
"David Harel"
] |
2024-06-28T15:34:26Z
|
2024-05-23T02:14:17Z
|
2406.19997
|
Wavelets Are All You Need for Autoregressive Image Generation
|
In this paper, we take a new approach to autoregressive image generation that is based on two main ingredients. The first is wavelet image coding, which allows to tokenize the visual details of an image from coarse to fine details by ordering the information starting with the most significant bits of the most significant wavelet coefficients. The second is a variant of a language transformer whose architecture is re-designed and optimized for token sequences in this 'wavelet language'. The transformer learns the significant statistical correlations within a token sequence, which are the manifestations of well-known correlations between the wavelet subbands at various resolutions. We show experimental results with conditioning on the generation process.
|
http://arxiv.org/pdf/2406.19997v1
|
[
"Wael Mattar",
"Idan Levy",
"Nir Sharon",
"Shai Dekel"
] |
2024-06-28T15:32:59Z
|
2024-06-28T15:32:59Z
|
2406.19995
|
Single Parent Family: A Spectrum of Family Members from a Single
Pre-Trained Foundation Model
|
This paper introduces a novel method of Progressive Low Rank Decomposition (PLRD) tailored for the compression of large language models. Our approach leverages a pre-trained model, which is then incrementally decompressed to smaller sizes using progressively lower ranks. This method allows for significant reductions in computational overhead and energy consumption, as subsequent models are derived from the original without the need for retraining from scratch. We detail the implementation of PLRD, which strategically decreases the tensor ranks, thus optimizing the trade-off between model performance and resource usage. The efficacy of PLRD is demonstrated through extensive experiments showing that models trained with PLRD method on only 1B tokens maintain comparable performance with traditionally trained models while using 0.1% of the tokens. The versatility of PLRD is highlighted by its ability to generate multiple model sizes from a single foundational model, adapting fluidly to varying computational and memory budgets. Our findings suggest that PLRD could set a new standard for the efficient scaling of LLMs, making advanced AI more feasible on diverse platforms.
|
http://arxiv.org/pdf/2406.19995v1
|
[
"Habib Hajimolahoseini",
"Mohammad Hassanpour",
"Foozhan Ataiefard",
"Boxing Chen",
"Yang Liu"
] |
2024-06-28T15:27:57Z
|
2024-06-28T15:27:57Z
|
2407.00141
|
Towards Secure and Efficient Data Scheduling for Vehicular Social
Networks
|
Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored for vehicular networks. Notwithstanding, the intricacies of orchestrating scheduling in vehicular social networks both effectively and efficiently remain formidable. This paper introduces an innovative learning-based algorithm for scheduling data transmission that prioritizes efficiency and security within vehicular social networks. The algorithm first uses a specifically constructed neural network to enhance data processing capabilities. After this, it incorporates a Q-learning paradigm during the data transmission phase to optimize the information exchange, the privacy of which is safeguarded by differential privacy through the communication process. Comparative experiments demonstrate the superior performance of the proposed Q-learning enhanced scheduling algorithm relative to existing state-of-the-art scheduling algorithms in the context of vehicular social networks.
|
http://arxiv.org/pdf/2407.00141v1
|
[
"Youhua Xia",
"Tiehua Zhang",
"Jiong Jin",
"Ying He",
"Fei Yu"
] |
2024-06-28T15:20:50Z
|
2024-06-28T15:20:50Z
|
2312.00125
|
Scalable Bayesian uncertainty quantification with data-driven priors for
radio interferometric imaging
|
Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensitivity. One key to unlocking their potential resides in handling the deluge and complexity of incoming data. This challenge requires building radio interferometric imaging methods that can cope with the massive data sizes and provide high-quality image reconstructions with uncertainty quantification (UQ). This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings. Our model, rooted in the Bayesian framework, uses a physically motivated model for the likelihood. The model exploits a data-driven convex prior, which can encode complex information learned implicitly from simulations and guarantee the log-concavity of the posterior. We leverage probability concentration phenomena of high-dimensional log-concave posteriors that let us obtain information about the posterior, avoiding MCMC sampling techniques. We rely on convex optimisation methods to compute the MAP estimation, which is known to be faster and better scale with dimension than MCMC sampling strategies. Our method allows us to compute local credible intervals, i.e., Bayesian error bars, and perform hypothesis testing of structure on the reconstructed image. In addition, we propose a novel blazing-fast method to compute pixel-wise uncertainties at different scales. We demonstrate our method by reconstructing radio-interferometric images in a simulated setting and carrying out fast and scalable UQ, which we validate with MCMC sampling. Our method shows an improved image quality and more meaningful uncertainties than the benchmark method based on a sparsity-promoting prior. QuantifAI's source code: https://github.com/astro-informatics/QuantifAI.
|
http://arxiv.org/pdf/2312.00125v2
|
[
"Tobías I. Liaudat",
"Matthijs Mars",
"Matthew A. Price",
"Marcelo Pereyra",
"Marta M. Betcke",
"Jason D. McEwen"
] |
2024-06-28T15:17:05Z
|
2023-11-30T19:00:02Z
|
2402.11658
|
Dynamic planning in hierarchical active inference
|
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behavior could be explained in terms of an active inferential process - either as discrete decision-making or continuous motor control - inspiring innovative solutions in robotics and artificial intelligence. Still, the literature lacks a comprehensive outlook on how to effectively plan actions in changing environments. Setting ourselves the goal of modeling tool use, we delve into the topic of dynamic planning in active inference, keeping in mind two crucial aspects of biological goal-directed behavior: the capacity to understand and exploit affordances for object manipulation, and to learn the hierarchical interactions between the self and the environment, including other agents. We start from a simple unit and gradually describe more advanced structures, comparing recently proposed design choices and providing basic examples for each section. This study distances itself from traditional views centered on neural networks and reinforcement learning, and points toward a yet unexplored direction in active inference: hybrid representations in hierarchical models.
|
http://arxiv.org/pdf/2402.11658v2
|
[
"Matteo Priorelli",
"Ivilin Peev Stoianov"
] |
2024-06-28T15:16:53Z
|
2024-02-18T17:32:53Z
|
2406.19983
|
Machine Learning Predictors for Min-Entropy Estimation
|
This study investigates the application of machine learning predictors for min-entropy estimation in Random Number Generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors, and indeed any predictor that leverages sequence correlations, primarily estimate average min-entropy, a metric not extensively studied in this context. We explore the relationship between average min-entropy and the traditional min-entropy, focusing on their dependence on the number of target bits being predicted. Utilizing data from Generalized Binary Autoregressive Models, a subset of Markov processes, we demonstrate that machine learning models (including a hybrid of convolutional and recurrent Long Short-Term Memory layers and the transformer-based GPT-2 model) outperform traditional NIST SP 800-90B predictors in certain scenarios. Our findings underscore the importance of considering the number of target bits in min-entropy assessment for RNGs and highlight the potential of machine learning approaches in enhancing entropy estimation techniques for improved cryptographic security.
|
http://arxiv.org/pdf/2406.19983v1
|
[
"Javier Blanco-Romero",
"Vicente Lorenzo",
"Florina Almenares Mendoza",
"Daniel Díaz-Sánchez"
] |
2024-06-28T15:15:01Z
|
2024-06-28T15:15:01Z
|
2405.03913
|
Digital Twin Calibration for Biological System-of-Systems: Cell Culture
Manufacturing Process
|
Biomanufacturing innovation relies on an efficient Design of Experiments (DoEs) to optimize processes and product quality. Traditional DoE methods, ignoring the underlying bioprocessing mechanisms, often suffer from a lack of interpretability and sample efficiency. This limitation motivates us to create a new optimal learning approach for digital twin model calibration. In this study, we consider the cell culture process multi-scale mechanistic model, also known as Biological System-of-Systems (Bio-SoS). This model with a modular design, composed of sub-models, allows us to integrate data across various production processes. To calibrate the Bio-SoS digital twin, we evaluate the mean squared error of model prediction and develop a computational approach to quantify the impact of parameter estimation error of individual sub-models on the prediction accuracy of digital twin, which can guide sample-efficient and interpretable DoEs.
|
http://arxiv.org/pdf/2405.03913v2
|
[
"Fuqiang Cheng",
"Wei Xie",
"Hua Zheng"
] |
2024-06-28T15:13:15Z
|
2024-05-07T00:22:13Z
|
2312.09969
|
Nearest Neighbor Sampling for Covariate Shift Adaptation
|
Many existing covariate shift adaptation methods estimate sample weights given to loss values to mitigate the gap between the source and the target distribution. However, estimating the optimal weights typically involves computationally expensive matrix inversion and hyper-parameter tuning. In this paper, we propose a new covariate shift adaptation method which avoids estimating the weights. The basic idea is to directly work on unlabeled target data, labeled according to the $k$-nearest neighbors in the source dataset. Our analysis reveals that setting $k = 1$ is an optimal choice. This property removes the necessity of tuning the only hyper-parameter $k$ and leads to a running time quasi-linear in the sample size. Our results include sharp rates of convergence for our estimator, with a tight control of the mean square error and explicit constants. In particular, the variance of our estimators has the same rate of convergence as for standard parametric estimation despite their non-parametric nature. The proposed estimator shares similarities with some matching-based treatment effect estimators used, e.g., in biostatistics, econometrics, and epidemiology. Our experiments show that it achieves drastic reduction in the running time with remarkable accuracy.
|
http://arxiv.org/pdf/2312.09969v2
|
[
"François Portier",
"Lionel Truquet",
"Ikko Yamane"
] |
2024-06-28T15:10:53Z
|
2023-12-15T17:28:09Z
|
2406.19980
|
Comparative Analysis of LSTM Neural Networks and Traditional Machine
Learning Models for Predicting Diabetes Patient Readmission
|
Diabetes mellitus is a chronic metabolic disorder that has emerged as one of the major health problems worldwide due to its high prevalence and serious complications, which are pricey to manage. Effective management requires good glycemic control and regular follow-up in the clinic; however, non-adherence to scheduled follow-ups is very common. This study uses the Diabetes 130-US Hospitals dataset for analysis and prediction of readmission patients by various traditional machine learning models, such as XGBoost, LightGBM, CatBoost, Decision Tree, and Random Forest, and also uses an in-house LSTM neural network for comparison. The quality of the data was assured by preprocessing it, and the performance evaluation for all these models was based on accuracy, precision, recall, and F1-score. LightGBM turned out to be the best traditional model, while XGBoost was the runner-up. The LSTM model suffered from overfitting despite high training accuracy. A major strength of LSTM is capturing temporal dependencies among the patient data. Further, SHAP values were used, which improved model interpretability, whereby key factors among them number of lab procedures and discharge disposition were identified as critical in the prediction of readmissions. This study demonstrates that model selection, validation, and interpretability are key steps in predictive healthcare modeling. This will help health providers design interventions for improved follow-up adherence and better management of diabetes.
|
http://arxiv.org/pdf/2406.19980v1
|
[
"Abolfazl Zarghani"
] |
2024-06-28T15:06:22Z
|
2024-06-28T15:06:22Z
|
2406.19976
|
ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting
|
Bilevel optimization has shown its utility across various machine learning settings, yet most algorithms in practice require second-order information, making it challenging to scale them up. Only recently, a paradigm of first-order algorithms emerged, capable of effectively addressing bilevel optimization problems. Nevertheless, the practical efficiency of this paradigm remains unverified, particularly in the context of large language models (LLMs). This paper introduces the first scalable instantiation of this paradigm called ScaleBiO, focusing on bilevel optimization for large-scale LLM data reweighting. By combining with a recently proposed memory-efficient training technique called LISA, our novel algorithm allows the paradigm to scale to 34-billion-parameter LLMs on eight A40 GPUs, marking the first successful application of bilevel optimization under practical scenarios for large-sized LLMs. Empirically, extensive experiments on data reweighting verify the effectiveness of ScaleBiO for different-scaled models, including GPT-2, LLaMA-3-8B, GPT-NeoX-20B, and Yi-34B, where bilevel optimization succeeds in filtering irrelevant data samples and selecting informative samples. Theoretically, ScaleBiO ensures the optimality of the learned data weights, along with a convergence guarantee matching the conventional first-order bilevel optimization paradigm on smooth and strongly convex objectives.
|
http://arxiv.org/pdf/2406.19976v1
|
[
"Rui Pan",
"Jipeng Zhang",
"Xingyuan Pan",
"Renjie Pi",
"Xiaoyu Wang",
"Tong Zhang"
] |
2024-06-28T15:03:08Z
|
2024-06-28T15:03:08Z
|
2406.19973
|
STLLaVA-Med: Self-Training Large Language and Vision Assistant for
Medical
|
Large Vision-Language Models (LVLMs) have shown significant potential in assisting medical diagnosis by leveraging extensive biomedical datasets. However, the advancement of medical image understanding and reasoning critically depends on building high-quality visual instruction data, which is costly and labor-intensive to obtain, particularly in the medical domain. To mitigate this data-starving issue, we introduce Self-Training Large Language and Vision Assistant for Medical (STLLaVA-Med). The proposed method is designed to train a policy model (an LVLM) capable of auto-generating medical visual instruction data to improve data efficiency, guided through Direct Preference Optimization (DPO). Specifically, a more powerful and larger LVLM (e.g., GPT-4o) is involved as a biomedical expert to oversee the DPO fine-tuning process on the auto-generated data, encouraging the policy model to align efficiently with human preferences. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks, demonstrating competitive zero-shot performance with the utilization of only 9% of the medical data.
|
http://arxiv.org/pdf/2406.19973v1
|
[
"Guohao Sun",
"Can Qin",
"Huazhu Fu",
"Linwei Wang",
"Zhiqiang Tao"
] |
2024-06-28T15:01:23Z
|
2024-06-28T15:01:23Z
|
2407.00140
|
ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal
Structural Behavior
|
External influences such as traffic and environmental factors induce vibrations in structures, leading to material degradation over time. These vibrations result in cracks due to the material's lack of plasticity compromising structural integrity. Detecting such damage requires the installation of vibration sensors to capture the internal dynamics. However, distinguishing relevant eigenmodes from external noise necessitates the use of Deep Learning models. The detection of changes in eigenmodes can be used to anticipate these shifts in material properties and to discern between normal and anomalous structural behavior. Eigenmodes, representing characteristic vibration patterns, provide insights into structural dynamics and deviations from expected states. Thus, we propose ModeConv to automatically capture and analyze changes in eigenmodes, facilitating effective anomaly detection in structures and material properties. In the conducted experiments, ModeConv demonstrates computational efficiency improvements, resulting in reduced runtime for model calculations. The novel ModeConv neural network layer is tailored for temporal graph neural networks, in which every node represents one sensor. ModeConv employs a singular value decomposition based convolutional filter design for complex numbers and leverages modal transformation in lieu of Fourier or Laplace transformations in spectral graph convolutions. We include a mathematical complexity analysis illustrating the runtime reduction.
|
http://arxiv.org/pdf/2407.00140v1
|
[
"Melanie Schaller",
"Daniel Schlör",
"Andreas Hotho"
] |
2024-06-28T14:46:17Z
|
2024-06-28T14:46:17Z
|
2406.19958
|
The Computational Curse of Big Data for Bayesian Additive Regression
Trees: A Hitting Time Analysis
|
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performance is supported by theoretical guarantees that its posterior distribution concentrates around the true regression function at optimal rates under various data generative settings and for appropriate prior choices. In this paper, we show that the BART sampler often converges slowly, confirming empirical observations by other researchers. Assuming discrete covariates, we show that, while the BART posterior concentrates on a set comprising all optimal tree structures (smallest bias and complexity), the Markov chain's hitting time for this set increases with $n$ (training sample size), under several common data generative settings. As $n$ increases, the approximate BART posterior thus becomes increasingly different from the exact posterior (for the same number of MCMC samples), contrasting with earlier concentration results on the exact posterior. This contrast is highlighted by our simulations showing worsening frequentist undercoverage for approximate posterior intervals and a growing ratio between the MSE of the approximate posterior and that obtainable by artificially improving convergence via averaging multiple sampler chains. Finally, based on our theoretical insights, possibilities are discussed to improve the BART sampler convergence performance.
|
http://arxiv.org/pdf/2406.19958v1
|
[
"Yan Shuo Tan",
"Omer Ronen",
"Theo Saarinen",
"Bin Yu"
] |
2024-06-28T14:45:29Z
|
2024-06-28T14:45:29Z
|
2406.19948
|
Kolmogorov-Smirnov GAN
|
We propose a novel deep generative model, the Kolmogorov-Smirnov Generative Adversarial Network (KSGAN). Unlike existing approaches, KSGAN formulates the learning process as a minimization of the Kolmogorov-Smirnov (KS) distance, generalized to handle multivariate distributions. This distance is calculated using the quantile function, which acts as the critic in the adversarial training process. We formally demonstrate that minimizing the KS distance leads to the trained approximate distribution aligning with the target distribution. We propose an efficient implementation and evaluate its effectiveness through experiments. The results show that KSGAN performs on par with existing adversarial methods, exhibiting stability during training, resistance to mode dropping and collapse, and tolerance to variations in hyperparameter settings. Additionally, we review the literature on the Generalized KS test and discuss the connections between KSGAN and existing adversarial generative models.
|
http://arxiv.org/pdf/2406.19948v1
|
[
"Maciej Falkiewicz",
"Naoya Takeishi",
"Alexandros Kalousis"
] |
2024-06-28T14:30:14Z
|
2024-06-28T14:30:14Z
|
2402.05758
|
Latent variable model for high-dimensional point process with structured
missingness
|
Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process. While various solutions have been suggested, the majority of them have been designed to account for only one of these challenges. In this work, we propose a flexible and efficient latent-variable model that is capable of addressing all these limitations. Our approach utilizes Gaussian processes to capture temporal correlations between samples and their associated missingness masks as well as to model the underlying point process. We construct our model as a variational autoencoder together with deep neural network parameterised encoder and decoder models, and develop a scalable amortised variational inference approach for efficient model training. We demonstrate competitive performance using both simulated and real datasets.
|
http://arxiv.org/pdf/2402.05758v2
|
[
"Maksim Sinelnikov",
"Manuel Haussmann",
"Harri Lähdesmäki"
] |
2024-06-28T14:27:29Z
|
2024-02-08T15:41:48Z
|
2406.15612
|
Catastrophic-risk-aware reinforcement learning with
extreme-value-theory-based policy gradients
|
This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented.
|
http://arxiv.org/pdf/2406.15612v2
|
[
"Parisa Davar",
"Frédéric Godin",
"Jose Garrido"
] |
2024-06-28T14:23:49Z
|
2024-06-21T19:27:46Z
|
2406.19931
|
Decoupling General and Personalized Knowledge in Federated Learning via
Additive and Low-Rank Decomposition
|
To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as the latter can have a negative impact on collaboration if not removed. Existing PFL methods primarily adopt a parameter partitioning approach, where the parameters of a model are designated as one of two types: parameters shared with other clients to extract general knowledge and parameters retained locally to learn client-specific knowledge. However, as these two types of parameters are put together like a jigsaw puzzle into a single model during the training process, each parameter may simultaneously absorb both general and client-specific knowledge, thus struggling to separate the two types of knowledge effectively. In this paper, we introduce FedDecomp, a simple but effective PFL paradigm that employs parameter additive decomposition to address this issue. Instead of assigning each parameter of a model as either a shared or personalized one, FedDecomp decomposes each parameter into the sum of two parameters: a shared one and a personalized one, thus achieving a more thorough decoupling of shared and personalized knowledge compared to the parameter partitioning method. In addition, as we find that retaining local knowledge of specific clients requires much lower model capacity compared with general knowledge across all clients, we let the matrix containing personalized parameters be low rank during the training process. Moreover, a new alternating training strategy is proposed to further improve the performance. Experimental results across multiple datasets and varying degrees of data heterogeneity demonstrate that FedDecomp outperforms state-of-the-art methods up to 4.9%.
|
http://arxiv.org/pdf/2406.19931v1
|
[
"Xinghao Wu",
"Xuefeng Liu",
"Jianwei Niu",
"Haolin Wang",
"Shaojie Tang",
"Guogang Zhu",
"Hao Su"
] |
2024-06-28T14:01:22Z
|
2024-06-28T14:01:22Z
|
2303.17001
|
The G-invariant graph Laplacian
|
Graph Laplacian based algorithms for data lying on a manifold have been proven effective for tasks such as dimensionality reduction, clustering, and denoising. In this work, we consider data sets whose data points lie on a manifold that is closed under the action of a known unitary matrix Lie group G. We propose to construct the graph Laplacian by incorporating the distances between all the pairs of points generated by the action of G on the data set. We deem the latter construction the ``G-invariant Graph Laplacian'' (G-GL). We show that the G-GL converges to the Laplace-Beltrami operator on the data manifold, while enjoying a significantly improved convergence rate compared to the standard graph Laplacian which only utilizes the distances between the points in the given data set. Furthermore, we show that the G-GL admits a set of eigenfunctions that have the form of certain products between the group elements and eigenvectors of certain matrices, which can be estimated from the data efficiently using FFT-type algorithms. We demonstrate our construction and its advantages on the problem of filtering data on a noisy manifold closed under the action of the special unitary group SU(2).
|
http://arxiv.org/pdf/2303.17001v4
|
[
"Eitan Rosen",
"Paulina Hoyos",
"Xiuyuan Cheng",
"Joe Kileel",
"Yoel Shkolnisky"
] |
2024-06-28T13:40:00Z
|
2023-03-29T20:07:07Z
|
2405.08498
|
Learning Decision Policies with Instrumental Variables through Double
Machine Learning
|
A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to estimate the causal effect. Naively plugging the estimator can cause heavy bias in the second stage, especially when regularisation bias is present in the first stage estimator. We propose DML-IV, a non-linear IV regression method that reduces the bias in two-stage IV regressions and effectively learns high-performing policies. We derive a novel learning objective to reduce bias and design the DML-IV algorithm following the double/debiased machine learning (DML) framework. The learnt DML-IV estimator has strong convergence rate and $O(N^{-1/2})$ suboptimality guarantees that match those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV regression benchmarks and learns high-performing policies in the presence of instruments.
|
http://arxiv.org/pdf/2405.08498v3
|
[
"Daqian Shao",
"Ashkan Soleymani",
"Francesco Quinzan",
"Marta Kwiatkowska"
] |
2024-06-28T13:31:48Z
|
2024-05-14T10:55:04Z
|
2406.17295
|
MatText: Do Language Models Need More than Text & Scale for Materials
Modeling?
|
Effectively representing materials as text has the potential to leverage the vast advancements of large language models (LLMs) for discovering new materials. While LLMs have shown remarkable success in various domains, their application to materials science remains underexplored. A fundamental challenge is the lack of understanding of how to best utilize text-based representations for materials modeling. This challenge is further compounded by the absence of a comprehensive benchmark to rigorously evaluate the capabilities and limitations of these text representations in capturing the complexity of material systems. To address this gap, we propose MatText, a suite of benchmarking tools and datasets designed to systematically evaluate the performance of language models in modeling materials. MatText encompasses nine distinct text-based representations for material systems, including several novel representations. Each representation incorporates unique inductive biases that capture relevant information and integrate prior physical knowledge about materials. Additionally, MatText provides essential tools for training and benchmarking the performance of language models in the context of materials science. These tools include standardized dataset splits for each representation, probes for evaluating sensitivity to geometric factors, and tools for seamlessly converting crystal structures into text. Using MatText, we conduct an extensive analysis of the capabilities of language models in modeling materials. Our findings reveal that current language models consistently struggle to capture the geometric information crucial for materials modeling across all representations. Instead, these models tend to leverage local information, which is emphasized in some of our novel representations. Our analysis underscores MatText's ability to reveal shortcomings of text-based methods for materials design.
|
http://arxiv.org/pdf/2406.17295v2
|
[
"Nawaf Alampara",
"Santiago Miret",
"Kevin Maik Jablonka"
] |
2024-06-28T13:28:04Z
|
2024-06-25T05:45:07Z
|
2406.01317
|
The Intelligible and Effective Graph Neural Additive Networks
|
Graph Neural Networks (GNNs) have emerged as the predominant approach for learning over graph-structured data. However, most GNNs operate as black-box models and require post-hoc explanations, which may not suffice in high-stakes scenarios where transparency is crucial. In this paper, we present a GNN that is interpretable by design. Our model, Graph Neural Additive Network (GNAN), is a novel extension of the interpretable class of Generalized Additive Models, and can be visualized and fully understood by humans. GNAN is designed to be fully interpretable, allowing both global and local explanations at the feature and graph levels through direct visualization of the model. These visualizations describe the exact way the model uses the relationships between the target variable, the features, and the graph. We demonstrate the intelligibility of GNANs in a series of examples on different tasks and datasets. In addition, we show that the accuracy of GNAN is on par with black-box GNNs, making it suitable for critical applications where transparency is essential, alongside high accuracy.
|
http://arxiv.org/pdf/2406.01317v2
|
[
"Maya Bechler-Speicher",
"Amir Globerson",
"Ran Gilad-Bachrach"
] |
2024-06-28T13:27:36Z
|
2024-06-03T13:29:36Z
|
2406.14862
|
LatentExplainer: Explaining Latent Representations in Deep Generative
Models with Multi-modal Foundation Models
|
Deep generative models like VAEs and diffusion models have advanced various generation tasks by leveraging latent variables to learn data distributions and generate high-quality samples. Despite the field of explainable AI making strides in interpreting machine learning models, understanding latent variables in generative models remains challenging. This paper introduces LatentExplainer, a framework for automatically generating semantically meaningful explanations of latent variables in deep generative models. LatentExplainer tackles three main challenges: inferring the meaning of latent variables, aligning explanations with inductive biases, and handling varying degrees of explainability. By perturbing latent variables and interpreting changes in generated data, the framework provides a systematic approach to understanding and controlling the data generation process, enhancing the transparency and interpretability of deep generative models. We evaluate our proposed method on several real-world and synthetic datasets, and the results demonstrate superior performance in generating high-quality explanations of latent variables.
|
http://arxiv.org/pdf/2406.14862v3
|
[
"Mengdan Zhu",
"Raasikh Kanjiani",
"Jiahui Lu",
"Andrew Choi",
"Qirui Ye",
"Liang Zhao"
] |
2024-06-28T13:19:37Z
|
2024-06-21T04:39:03Z
|
2404.07049
|
Towards Learning Stochastic Population Models by Gradient Descent
|
Increasing effort is put into the development of methods for learning mechanistic models from data. This task entails not only the accurate estimation of parameters but also a suitable model structure. Recent work on the discovery of dynamical systems formulates this problem as a linear equation system. Here, we explore several simulation-based optimization approaches, which allow much greater freedom in the objective formulation and weaker conditions on the available data. We show that even for relatively small stochastic population models, simultaneous estimation of parameters and structure poses major challenges for optimization procedures. Particularly, we investigate the application of the local stochastic gradient descent method, commonly used for training machine learning models. We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty. We give an outlook on how this challenge can be overcome.
|
http://arxiv.org/abs/2404.07049v2
|
[
"Justin N. Kreikemeyer",
"Philipp Andelfinger",
"Adelinde M. Uhrmacher"
] |
2024-06-28T13:14:13Z
|
2024-04-10T14:38:58Z
|
2406.19900
|
`Just One More Sensor is Enough' -- Iterative Water Leak Localization
with Physical Simulation and a Small Number of Pressure Sensors
|
In this article, we propose an approach to leak localisation in a complex water delivery grid with the use of data from physical simulation (e.g. EPANET software). This task is usually achieved by a network of multiple water pressure sensors and analysis of the so-called sensitivity matrix of pressure differences between the network's simulated data and actual data of the network affected by the leak. However, most algorithms using this approach require a significant number of pressure sensors -- a condition that is not easy to fulfil in the case of many less equipped networks. Therefore, we answer the question of whether leak localisation is possible by utilising very few sensors but having the ability to relocate one of them. Our algorithm is based on physical simulations (EPANET software) and an iterative scheme for mobile sensor relocation. The experiments show that the proposed system can equalise the low number of sensors with adjustments made for their positioning, giving a very good approximation of leak's position both in simulated cases and real-life example taken from BattLeDIM competition L-Town data.
|
http://arxiv.org/pdf/2406.19900v1
|
[
"Michał Cholewa",
"Michał Romaszewski",
"Przemysław Głomb",
"Katarzyna Kołodziej",
"Michał Gorawski",
"Jakub Koral",
"Wojciech Koral",
"Andrzej Madej",
"Kryspin Musioł"
] |
2024-06-28T13:10:13Z
|
2024-06-28T13:10:13Z
|
2406.19897
|
FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert
Rules
|
A method for solving concept-based learning (CBL) problem is proposed. The main idea behind the method is to divide each concept-annotated image into patches, to transform the patches into embeddings by using an autoencoder, and to cluster the embeddings assuming that each cluster will mainly contain embeddings of patches with certain concepts. To find concepts of a new image, the method implements the frequentist inference by computing prior and posterior probabilities of concepts based on rates of patches from images with certain values of the concepts. Therefore, the proposed method is called the Frequentist Inference CBL (FI-CBL). FI-CBL allows us to incorporate the expert rules in the form of logic functions into the inference procedure. An idea behind the incorporation is to update prior and conditional probabilities of concepts to satisfy the rules. The method is transparent because it has an explicit sequence of probabilistic calculations and a clear frequency interpretation. Numerical experiments show that FI-CBL outperforms the concept bottleneck model in cases when the number of training data is small. The code of proposed algorithms is publicly available.
|
http://arxiv.org/pdf/2406.19897v1
|
[
"Lev V. Utkin",
"Andrei V. Konstantinov",
"Stanislav R. Kirpichenko"
] |
2024-06-28T13:05:17Z
|
2024-06-28T13:05:17Z
|
2308.02029
|
Deep Maxout Network-based Feature Fusion and Political Tangent Search
Optimizer enabled Transfer Learning for Thalassemia Detection
|
Thalassemia is a heritable blood disorder which is the outcome of a genetic defect causing lack of production of hemoglobin polypeptide chains. However, there is less understanding of the precise frequency as well as sharing in these areas. Knowing about the frequency of thalassemia occurrence and dependable mutations is thus a significant step in preventing, controlling, and treatment planning. Here, Political Tangent Search Optimizer based Transfer Learning (PTSO_TL) is introduced for thalassemia detection. Initially, input data obtained from a particular dataset is normalized in the data normalization stage. Quantile normalization is utilized in the data normalization stage, and the data are then passed to the feature fusion phase, in which Weighted Euclidean Distance with Deep Maxout Network (DMN) is utilized. Thereafter, data augmentation is performed using the oversampling method to increase data dimensionality. Lastly, thalassemia detection is carried out by TL, wherein a convolutional neural network (CNN) is utilized with hyperparameters from a trained model such as Xception. TL is tuned by PTSO, and the training algorithm PTSO is presented by merging of Political Optimizer (PO) and Tangent Search Algorithm (TSA). Furthermore, PTSO_TL obtained maximal precision, recall, and f-measure values of about 94.3%, 96.1%, and 95.2%, respectively.
|
http://arxiv.org/pdf/2308.02029v3
|
[
"Hemn Barzan Abdalla",
"Awder Ahmed",
"Guoquan Li",
"Nasser Mustafa",
"Abdur Rashid Sangi"
] |
2024-06-28T13:02:49Z
|
2023-08-03T20:45:11Z
|
2307.03565
|
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
|
Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate models that suffer from scalability issues and are sensitive to observations with different scales and noise types across tasks. Moreover, they often overlook the uncertainty associated with task similarity. This leads to unreliable task adaptation when only limited observations are obtained or when the new tasks differ significantly from the related tasks. To address these limitations, we propose a novel meta-learning BO approach that bypasses the surrogate model and directly learns the utility of queries across tasks. Our method explicitly models task uncertainty and includes an auxiliary model to enable robust adaptation to new tasks. Extensive experiments show that our method demonstrates strong anytime performance and outperforms state-of-the-art meta-learning BO methods in various benchmarks.
|
http://arxiv.org/pdf/2307.03565v3
|
[
"Jiarong Pan",
"Stefan Falkner",
"Felix Berkenkamp",
"Joaquin Vanschoren"
] |
2024-06-28T12:55:35Z
|
2023-07-07T12:57:10Z
|
2406.18328
|
PDFA Distillation via String Probability Queries
|
Probabilistic deterministic finite automata (PDFA) are discrete event systems modeling conditional probabilities over languages: Given an already seen sequence of tokens they return the probability of tokens of interest to appear next. These types of models have gained interest in the domain of explainable machine learning, where they are used as surrogate models for neural networks trained as language models. In this work we present an algorithm to distill PDFA from neural networks. Our algorithm is a derivative of the L# algorithm and capable of learning PDFA from a new type of query, in which the algorithm infers conditional probabilities from the probability of the queried string to occur. We show its effectiveness on a recent public dataset by distilling PDFA from a set of trained neural networks.
|
http://arxiv.org/pdf/2406.18328v2
|
[
"Robert Baumgartner",
"Sicco Verwer"
] |
2024-06-28T12:45:52Z
|
2024-06-26T13:16:40Z
|
2406.19881
|
Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in
Low-Cost UAVs
|
This paper explores the critical issue of enhancing cybersecurity measures for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed Denial of Service (DDoS) attacks. In the current work, we have explored three variants of DDoS attacks, namely Transmission Control Protocol (TCP), Internet Control Message Protocol (ICMP), and TCP + ICMP flooding attacks, and developed a detection mechanism that runs on the companion computer of the UAV system. As a part of the detection mechanism, we have evaluated various machine learning, and deep learning algorithms, such as XGBoost, Isolation Forest, Long Short-Term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), LSTM with attention, Bi-LSTM with attention, and Time Series Transformer (TST) in terms of various classification metrics. Our evaluation reveals that algorithms with attention mechanisms outperform their counterparts in general, and TST stands out as the most efficient model with a run time of 0.1 seconds. TST has demonstrated an F1 score of 0.999, 0.997, and 0.943 for TCP, ICMP, and TCP + ICMP flooding attacks respectively. In this work, we present the necessary steps required to build an on-board DDoS detection mechanism. Further, we also present the ablation study to identify the best TST hyperparameters for DDoS detection, and we have also underscored the advantage of adapting learnable positional embeddings in TST for DDoS detection with an improvement in F1 score from 0.94 to 0.99.
|
http://arxiv.org/pdf/2406.19881v1
|
[
"Ashish Sharma",
"SVSLN Surya Suhas Vaddhiparthy",
"Sai Usha Goparaju",
"Deepak Gangadharan",
"Harikumar Kandath"
] |
2024-06-28T12:44:01Z
|
2024-06-28T12:44:01Z
|
2406.19871
|
Koopman based trajectory model and computation offloading for high
mobility paradigm in ISAC enabled IoT system
|
User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a solution, offloading computationally intensive tasks to edge cloud servers, reducing battery drain compared to local processing. The upcoming integrated sensing and communication in mobile communication may improve the trajectory prediction and processing delays. This study proposes a greedy resource allocation optimization strategy for multi-user networks to minimize aggregate energy usage. Numerical results show potential improvement at 33% for every 1000 iteration. Addressing prediction model division and velocity accuracy issues is crucial for better results. A plan for further improvement and achieving objectives is outlined for the upcoming work phase.
|
http://arxiv.org/pdf/2406.19871v1
|
[
"Minh-Tuan Tran"
] |
2024-06-28T12:26:28Z
|
2024-06-28T12:26:28Z
|
2406.19861
|
Operator World Models for Reinforcement Learning
|
Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions. We address this challenge by introducing a novel approach based on learning a world model of the environment using conditional mean embeddings. We then leverage the operatorial formulation of RL to express the action-value function in terms of this quantity in closed form via matrix operations. Combining these estimators with PMD leads to POWR, a new RL algorithm for which we prove convergence rates to the global optimum. Preliminary experiments in finite and infinite state settings support the effectiveness of our method.
|
http://arxiv.org/pdf/2406.19861v1
|
[
"Pietro Novelli",
"Marco Pratticò",
"Massimiliano Pontil",
"Carlo Ciliberto"
] |
2024-06-28T12:05:47Z
|
2024-06-28T12:05:47Z
|
2401.13699
|
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A
Comprehensive Survey
|
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare, attracting extensive attentions to IoT-healthcare services. Meanwhile, the human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body in the digital world and reflect its physical status in real time. Naturally, HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed, simulating the outcomes and guiding the practical treatments. However, successfully establishing HDT requires high-fidelity virtual modeling and strong information interactions but possibly with scarce, biased and noisy data. Fortunately, a recent popular technology called generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data. This survey particularly focuses on the implementation of GAI-driven HDT in IoT-healthcare. We start by introducing the background of IoT-healthcare and the potential of GAI-driven HDT. Then, we delve into the fundamental techniques and present the overall framework of GAI-driven HDT. After that, we explore the realization of GAI-driven HDT in detail, including GAI-enabled data acquisition, communication, data management, digital modeling, and data analysis. Besides, we discuss typical IoT-healthcare applications that can be revolutionized by GAI-driven HDT, namely personalized health monitoring and diagnosis, personalized prescription, and personalized rehabilitation. Finally, we conclude this survey by highlighting some future research directions.
|
http://arxiv.org/pdf/2401.13699v2
|
[
"Jiayuan Chen",
"You Shi",
"Changyan Yi",
"Hongyang Du",
"Jiawen Kang",
"Dusit Niyato"
] |
2024-06-28T11:49:52Z
|
2024-01-22T03:17:41Z
|
2306.02766
|
Networked Communication for Decentralised Agents in Mean-Field Games
|
We introduce networked communication to the mean-field game framework, in particular to oracle-free settings where $N$ decentralised agents learn along a single, non-episodic run of the empirical system. We prove that our architecture, with only a few reasonable assumptions about network structure, has sample guarantees bounded between those of the centralised- and independent-learning cases. We discuss how the sample guarantees of the three theoretical algorithms do not actually result in practical convergence. We therefore show that in practical settings where the theoretical parameters are not observed (leading to poor estimation of the Q-function), our communication scheme significantly accelerates convergence over the independent case (and often even the centralised case), without relying on the assumption of a centralised learner. We contribute further practical enhancements to all three theoretical algorithms, allowing us to present their first empirical demonstrations. Our experiments confirm that we can remove several of the theoretical assumptions of the algorithms, and display the empirical convergence benefits brought by our new networked communication. We additionally show that the networked approach has significant advantages, over both the centralised and independent alternatives, in terms of robustness to unexpected learning failures and to changes in population size.
|
http://arxiv.org/pdf/2306.02766v3
|
[
"Patrick Benjamin",
"Alessandro Abate"
] |
2024-06-28T11:39:10Z
|
2023-06-05T10:45:39Z
|
2312.03511
|
Kandinsky 3.0 Technical Report
|
We present Kandinsky 3.0, a large-scale text-to-image generation model based on latent diffusion, continuing the series of text-to-image Kandinsky models and reflecting our progress to achieve higher quality and realism of image generation. In this report we describe the architecture of the model, the data collection procedure, the training technique, and the production system for user interaction. We focus on the key components that, as we have identified as a result of a large number of experiments, had the most significant impact on improving the quality of our model compared to the others. We also describe extensions and applications of our model, including super resolution, inpainting, image editing, image-to-video generation, and a distilled version of Kandinsky 3.0 - Kandinsky 3.1, which does inference in 4 steps of the reverse process and 20 times faster without visual quality decrease. By side-by-side human preferences comparison, Kandinsky becomes better in text understanding and works better on specific domains. The code is available at https://github.com/ai-forever/Kandinsky-3
|
http://arxiv.org/pdf/2312.03511v3
|
[
"Vladimir Arkhipkin",
"Andrei Filatov",
"Viacheslav Vasilev",
"Anastasia Maltseva",
"Said Azizov",
"Igor Pavlov",
"Julia Agafonova",
"Andrey Kuznetsov",
"Denis Dimitrov"
] |
2024-06-28T11:23:11Z
|
2023-12-06T14:13:38Z
|
2406.19832
|
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for
Graph Classification
|
Recent works have introduced GNN-to-MLP knowledge distillation (KD) frameworks to combine both GNN's superior performance and MLP's fast inference speed. However, existing KD frameworks are primarily designed for node classification within single graphs, leaving their applicability to graph classification largely unexplored. Two main challenges arise when extending KD for node classification to graph classification: (1) The inherent sparsity of learning signals due to soft labels being generated at the graph level; (2) The limited expressiveness of student MLPs, especially in datasets with limited input feature spaces. To overcome these challenges, we introduce MuGSI, a novel KD framework that employs Multi-granularity Structural Information for graph classification. Specifically, we propose multi-granularity distillation loss in MuGSI to tackle the first challenge. This loss function is composed of three distinct components: graph-level distillation, subgraph-level distillation, and node-level distillation. Each component targets a specific granularity of the graph structure, ensuring a comprehensive transfer of structural knowledge from the teacher model to the student model. To tackle the second challenge, MuGSI proposes to incorporate a node feature augmentation component, thereby enhancing the expressiveness of the student MLPs and making them more capable learners. We perform extensive experiments across a variety of datasets and different teacher/student model architectures. The experiment results demonstrate the effectiveness, efficiency, and robustness of MuGSI. Codes are publicly available at: textbf{url{https://github.com/tianyao-aka/MuGSI}.}
|
http://arxiv.org/abs/2406.19832v1
|
[
"Tianjun Yao",
"Jiaqi Sun",
"Defu Cao",
"Kun Zhang",
"Guangyi Chen"
] |
2024-06-28T11:11:16Z
|
2024-06-28T11:11:16Z
|
2406.19827
|
Towards Stable and Storage-efficient Dataset Distillation: Matching
Convexified Trajectory
|
The rapid evolution of deep learning and large language models has led to an exponential growth in the demand for training data, prompting the development of Dataset Distillation methods to address the challenges of managing large datasets. Among these, Matching Training Trajectories (MTT) has been a prominent approach, which replicates the training trajectory of an expert network on real data with a synthetic dataset. However, our investigation found that this method suffers from three significant limitations: 1. Instability of expert trajectory generated by Stochastic Gradient Descent (SGD); 2. Low convergence speed of the distillation process; 3. High storage consumption of the expert trajectory. To address these issues, we offer a new perspective on understanding the essence of Dataset Distillation and MTT through a simple transformation of the objective function, and introduce a novel method called Matching Convexified Trajectory (MCT), which aims to provide better guidance for the student trajectory. MCT leverages insights from the linearized dynamics of Neural Tangent Kernel methods to create a convex combination of expert trajectories, guiding the student network to converge rapidly and stably. This trajectory is not only easier to store, but also enables a continuous sampling strategy during distillation, ensuring thorough learning and fitting of the entire expert trajectory. Comprehensive experiments across three public datasets validate the superiority of MCT over traditional MTT methods.
|
http://arxiv.org/pdf/2406.19827v1
|
[
"Wenliang Zhong",
"Haoyu Tang",
"Qinghai Zheng",
"Mingzhu Xu",
"Yupeng Hu",
"Liqiang Nie"
] |
2024-06-28T11:06:46Z
|
2024-06-28T11:06:46Z
|
2406.19825
|
Reinforcement Learning for Efficient Design and Control Co-optimisation
of Energy Systems
|
The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into energy systems. This study tackles this issue by introducing a novel reinforcement learning (RL) framework tailored for the co-optimisation of design and control in energy systems. Traditionally, the integration of renewable sources in the energy sector has relied on complex mathematical modelling and sequential processes. By leveraging RL's model-free capabilities, the framework eliminates the need for explicit system modelling. By optimising both control and design policies jointly, the framework enhances the integration of renewable sources and improves system efficiency. This contribution paves the way for advanced RL applications in energy management, leading to more efficient and effective use of renewable energy sources.
|
http://arxiv.org/pdf/2406.19825v1
|
[
"Marine Cauz",
"Adrien Bolland",
"Nicolas Wyrsch",
"Christophe Ballif"
] |
2024-06-28T11:01:02Z
|
2024-06-28T11:01:02Z
|
2212.03080
|
Straggler-Resilient Differentially-Private Decentralized Learning
|
We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by decentralization by Cyffers and Bellet to include overall training latency--comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. A trade-off between overall training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme, is identified and empirically validated for logistic regression on a real-world dataset and for image classification using the MNIST and CIFAR-10 datasets.
|
http://arxiv.org/pdf/2212.03080v3
|
[
"Yauhen Yakimenka",
"Chung-Wei Weng",
"Hsuan-Yin Lin",
"Eirik Rosnes",
"Jörg Kliewer"
] |
2024-06-28T10:52:37Z
|
2022-12-06T15:50:28Z
|
2407.00134
|
A Simple Attention-Based Mechanism for Bimodal Emotion Classification
|
Big data contain rich information for machine learning algorithms to utilize when learning important features during classification tasks. Human beings express their emotion using certain words, speech (tone, pitch, speed) or facial expression. Artificial Intelligence approach to emotion classification are largely based on learning from textual information. However, public datasets containing text and speech data provide sufficient resources to train machine learning algorithms for the tack of emotion classification. In this paper, we present novel bimodal deep learning-based architectures enhanced with attention mechanism trained and tested on text and speech data for emotion classification. We report details of different deep learning based architectures and show the performance of each architecture including rigorous error analyses. Our finding suggests that deep learning based architectures trained on different types of data (text and speech) outperform architectures trained only on text or speech. Our proposed attention-based bimodal architecture outperforms several state-of-the-art systems in emotion classification.
|
http://arxiv.org/pdf/2407.00134v1
|
[
"Mazen Elabd",
"Sardar Jaf"
] |
2024-06-28T10:43:02Z
|
2024-06-28T10:43:02Z
|
2406.19807
|
Deceptive Diffusion: Generating Synthetic Adversarial Examples
|
We introduce the concept of deceptive diffusion -- training a generative AI model to produce adversarial images. Whereas a traditional adversarial attack algorithm aims to perturb an existing image to induce a misclassificaton, the deceptive diffusion model can create an arbitrary number of new, misclassified images that are not directly associated with training or test images. Deceptive diffusion offers the possibility of strengthening defence algorithms by providing adversarial training data at scale, including types of misclassification that are otherwise difficult to find. In our experiments, we also investigate the effect of training on a partially attacked data set. This highlights a new type of vulnerability for generative diffusion models: if an attacker is able to stealthily poison a portion of the training data, then the resulting diffusion model will generate a similar proportion of misleading outputs.
|
http://arxiv.org/pdf/2406.19807v1
|
[
"Lucas Beerens",
"Catherine F. Higham",
"Desmond J. Higham"
] |
2024-06-28T10:30:46Z
|
2024-06-28T10:30:46Z
|
2406.19801
|
MulTi-Wise Sampling: Trading Uniform T-Wise Feature Interaction Coverage
for Smaller Samples
|
Ensuring the functional safety of highly configurable systems often requires testing representative subsets of all possible configurations to reduce testing effort and save resources. The ratio of covered t-wise feature interactions (i.e., T-Wise Feature Interaction Coverage) is a common criterion for determining whether a subset of configurations is representative and capable of finding faults. Existing t-wise sampling algorithms uniformly cover t-wise feature interactions for all features, resulting in lengthy execution times and large sample sizes, particularly when large t-wise feature interactions are considered (i.e., high values of t). In this paper, we introduce a novel approach to t-wise feature interaction sampling, questioning the necessity of uniform coverage across all t-wise feature interactions, called emph{mulTiWise{}}. Our approach prioritizes between subsets of critical and non-critical features, considering higher t-values for subsets of critical features when generating a t-wise feature interaction sample. We evaluate our approach using subject systems from real-world applications, including busybox{}, soletta{}, fiasco{}, and uclibc{}. Our results show that sacrificing uniform t-wise feature interaction coverage between all features reduces the time needed to generate a sample and the resulting sample size. Hence, mulTiWise{} Sampling offers an alternative to existing approaches if knowledge about feature criticality is available.
|
http://arxiv.org/abs/2406.19801v1
|
[
"Tobias Pett",
"Sebastian Krieter",
"Thomas Thüm",
"Ina Schaefer"
] |
2024-06-28T10:16:10Z
|
2024-06-28T10:16:10Z
|
2406.16783
|
M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in
Large Language Models
|
Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT datasets have been introduced recently, they predominantly focus on high-resource languages like English. To better align LLMs across a broad spectrum of languages and tasks, we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual, Multi-turn instruction finetuning dataset, called M2Lingual. It is constructed by first selecting a diverse set of seed examples and then utilizing the proposed Evol taxonomy to convert these seeds into complex and challenging multi-turn instructions. We demonstrate the effectiveness of M2Lingual by training LLMs of varying sizes and showcasing the enhanced performance across a diverse set of languages. We contribute the 2 step Evol taxonomy with the guided generation code: https://github.com/ServiceNow/M2Lingual, as well as the first fully synthetic, general and task-oriented, multi-turn, multilingual dataset built with Evol - M2Lingual: https://huggingface.co/datasets/ServiceNow-AI/ M2Lingual - containing 182K total IFT pairs, covering 70 languages and 17+ NLP tasks.
|
http://arxiv.org/pdf/2406.16783v2
|
[
"Rishabh Maheshwary",
"Vikas Yadav",
"Hoang Nguyen",
"Khyati Mahajan",
"Sathwik Tejaswi Madhusudhan"
] |
2024-06-28T10:14:53Z
|
2024-06-24T16:45:13Z
|
2406.19800
|
Modeling the Real World with High-Density Visual Particle Dynamics
|
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles. To enable efficiency at this scale, we introduce a novel family of Point Cloud Transformers (PCTs) called Interlacers leveraging intertwined linear-attention Performer layers and graph-based neighbour attention layers. We demonstrate the capabilities of HD-VPD by modeling the dynamics of high degree-of-freedom bi-manual robots with two RGB-D cameras. Compared to the previous graph neural network approach, our Interlacer dynamics is twice as fast with the same prediction quality, and can achieve higher quality using 4x as many particles. We illustrate how HD-VPD can evaluate motion plan quality with robotic box pushing and can grasping tasks. See videos and particle dynamics rendered by HD-VPD at https://sites.google.com/view/hd-vpd.
|
http://arxiv.org/pdf/2406.19800v1
|
[
"William F. Whitney",
"Jacob Varley",
"Deepali Jain",
"Krzysztof Choromanski",
"Sumeet Singh",
"Vikas Sindhwani"
] |
2024-06-28T10:13:50Z
|
2024-06-28T10:13:50Z
|
2309.04268
|
Optimal Rate of Kernel Regression in Large Dimensions
|
We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i.e., $nasymp d^{gamma}$ for some $gamma >0$ ). We first build a general tool to characterize the upper bound and the minimax lower bound of kernel regression for large dimensional data through the Mendelson complexity $varepsilon_{n}^{2}$ and the metric entropy $bar{varepsilon}_{n}^{2}$ respectively. When the target function falls into the RKHS associated with a (general) inner product model defined on $mathbb{S}^{d}$, we utilize the new tool to show that the minimax rate of the excess risk of kernel regression is $n^{-1/2}$ when $nasymp d^{gamma}$ for $gamma =2, 4, 6, 8, cdots$. We then further determine the optimal rate of the excess risk of kernel regression for all the $gamma>0$ and find that the curve of optimal rate varying along $gamma$ exhibits several new phenomena including the multiple descent behavior and the periodic plateau behavior. As an application, For the neural tangent kernel (NTK), we also provide a similar explicit description of the curve of optimal rate. As a direct corollary, we know these claims hold for wide neural networks as well.
|
http://arxiv.org/pdf/2309.04268v2
|
[
"Weihao Lu",
"Haobo Zhang",
"Yicheng Li",
"Manyun Xu",
"Qian Lin"
] |
2024-06-28T09:59:08Z
|
2023-09-08T11:29:05Z
|
2402.03216
|
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity
Text Embeddings Through Self-Knowledge Distillation
|
In this paper, we present a new embedding model, called M3-Embedding, which is distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It can support more than 100 working languages, leading to new state-of-the-art performances on multi-lingual and cross-lingual retrieval tasks. It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval, which provides a unified model foundation for real-world IR applications. It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. The effective training of M3-Embedding involves the following technical contributions. We propose a novel self-knowledge distillation approach, where the relevance scores from different retrieval functionalities can be integrated as the teacher signal to enhance the training quality. We also optimize the batching strategy, enabling a large batch size and high training throughput to ensure the discriminativeness of embeddings. To the best of our knowledge, M3-Embedding is the first embedding model which realizes such a strong versatility. The model and code will be publicly available at https://github.com/FlagOpen/FlagEmbedding.
|
http://arxiv.org/pdf/2402.03216v4
|
[
"Jianlv Chen",
"Shitao Xiao",
"Peitian Zhang",
"Kun Luo",
"Defu Lian",
"Zheng Liu"
] |
2024-06-28T09:55:49Z
|
2024-02-05T17:26:49Z
|
2406.19792
|
Improving Performance Prediction of Electrolyte Formulations with
Transformer-based Molecular Representation Model
|
Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery electrolytes rely on complex interactions between the individual constituents. Consequently, a strategy that adeptly captures these relationships and forms a robust representation of the formulation is essential for integrating with machine learning models to predict properties accurately. In this paper, we introduce a novel approach leveraging a transformer-based molecular representation model to effectively and efficiently capture the representation of electrolyte formulations. The performance of the proposed approach is evaluated on two battery property prediction tasks and the results show superior performance compared to the state-of-the-art methods.
|
http://arxiv.org/pdf/2406.19792v1
|
[
"Indra Priyadarsini",
"Vidushi Sharma",
"Seiji Takeda",
"Akihiro Kishimoto",
"Lisa Hamada",
"Hajime Shinohara"
] |
2024-06-28T09:55:29Z
|
2024-06-28T09:55:29Z
|
2405.15613
|
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based
Approach
|
Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically require extensive human effort. This manual process has some limitations similar to those encountered in supervised learning, e.g., the crowd-sourced selection of data is costly and time-consuming, preventing scaling the dataset size. In this work, we consider the problem of automatic curation of high-quality datasets for self-supervised pre-training. We posit that such datasets should be large, diverse and balanced, and propose a clustering-based approach for building ones satisfying all these criteria. Our method involves successive and hierarchical applications of $k$-means on a large and diverse data repository to obtain clusters that distribute uniformly among data concepts, followed by a hierarchical, balanced sampling step from these clusters. Extensive experiments on three different data domains including web-based images, satellite images and text show that features trained on our automatically curated datasets outperform those trained on uncurated data while being on par or better than ones trained on manually curated data. Code is available at https://github.com/facebookresearch/ssl-data-curation.
|
http://arxiv.org/pdf/2405.15613v2
|
[
"Huy V. Vo",
"Vasil Khalidov",
"Timothée Darcet",
"Théo Moutakanni",
"Nikita Smetanin",
"Marc Szafraniec",
"Hugo Touvron",
"Camille Couprie",
"Maxime Oquab",
"Armand Joulin",
"Hervé Jégou",
"Patrick Labatut",
"Piotr Bojanowski"
] |
2024-06-28T09:22:38Z
|
2024-05-24T14:58:51Z
|
2406.19770
|
Self-Supervised Spatial-Temporal Normality Learning for Time Series
Anomaly Detection
|
Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning (DSN) module that learns the relative spatial relations between sequences in a feature space. By synthesizing these two modules, STEN learns expressive spatial-temporal representations for the normal patterns hidden in the time series data. Extensive experiments on five popular TSAD benchmarks show that STEN substantially outperforms state-of-the-art competing methods. Our code is available at https://github.com/mala-lab/STEN.
|
http://arxiv.org/pdf/2406.19770v1
|
[
"Yutong Chen",
"Hongzuo Xu",
"Guansong Pang",
"Hezhe Qiao",
"Yuan Zhou",
"Mingsheng Shang"
] |
2024-06-28T09:17:58Z
|
2024-06-28T09:17:58Z
|
2402.07158
|
Effort and Size Estimation in Software Projects with Large Language
Model-based Intelligent Interfaces
|
The advancement of Large Language Models (LLM) has also resulted in an equivalent proliferation in its applications. Software design, being one, has gained tremendous benefits in using LLMs as an interface component that extends fixed user stories. However, inclusion of LLM-based AI agents in software design often poses unexpected challenges, especially in the estimation of development efforts. Through the example of UI-based user stories, we provide a comparison against traditional methods and propose a new way to enhance specifications of natural language-based questions that allows for the estimation of development effort by taking into account data sources, interfaces and algorithms.
|
http://arxiv.org/pdf/2402.07158v2
|
[
"Claudionor N. Coelho Jr",
"Hanchen Xiong",
"Tushar Karayil",
"Sree Koratala",
"Rex Shang",
"Jacob Bollinger",
"Mohamed Shabar",
"Syam Nair"
] |
2024-06-28T08:57:39Z
|
2024-02-11T11:03:08Z
|
2406.15486
|
SampleAttention: Near-Lossless Acceleration of Long Context LLM
Inference with Adaptive Structured Sparse Attention
|
Large language models (LLMs) now support extremely long context windows, but the quadratic complexity of vanilla attention results in significantly long Time-to-First-Token (TTFT) latency. Existing approaches to address this complexity require additional pretraining or finetuning, and often sacrifice model accuracy. In this paper, we first provide both theoretical and empirical foundations for near-lossless sparse attention. We find dynamically capturing head-specific sparse patterns at runtime with low overhead is crucial. To address this, we propose SampleAttention, an adaptive structured and near-lossless sparse attention. Leveraging observed significant sparse patterns, SampleAttention attends to a fixed percentage of adjacent tokens to capture local window patterns, and employs a two-stage query-guided key-value filtering approach, which adaptively select a minimum set of key-values with low overhead, to capture column stripe patterns. Comprehensive evaluations show that SampleAttention can seamlessly replace vanilla attention in off-the-shelf LLMs with nearly no accuracy loss, and reduces TTFT by up to $2.42times$ compared with FlashAttention.
|
http://arxiv.org/pdf/2406.15486v2
|
[
"Qianchao Zhu",
"Jiangfei Duan",
"Chang Chen",
"Siran Liu",
"Xiuhong Li",
"Guanyu Feng",
"Xin Lv",
"Huanqi Cao",
"Xiao Chuanfu",
"Xingcheng Zhang",
"Dahua Lin",
"Chao Yang"
] |
2024-06-28T08:55:17Z
|
2024-06-17T11:05:15Z
|
2406.19753
|
Backdoor Attack in Prompt-Based Continual Learning
|
Prompt-based approaches offer a cutting-edge solution to data privacy issues in continual learning, particularly in scenarios involving multiple data suppliers where long-term storage of private user data is prohibited. Despite delivering state-of-the-art performance, its impressive remembering capability can become a double-edged sword, raising security concerns as it might inadvertently retain poisoned knowledge injected during learning from private user data. Following this insight, in this paper, we expose continual learning to a potential threat: backdoor attack, which drives the model to follow a desired adversarial target whenever a specific trigger is present while still performing normally on clean samples. We highlight three critical challenges in executing backdoor attacks on incremental learners and propose corresponding solutions: (1) emph{Transferability}: We employ a surrogate dataset and manipulate prompt selection to transfer backdoor knowledge to data from other suppliers; (2) emph{Resiliency}: We simulate static and dynamic states of the victim to ensure the backdoor trigger remains robust during intense incremental learning processes; and (3) emph{Authenticity}: We apply binary cross-entropy loss as an anti-cheating factor to prevent the backdoor trigger from devolving into adversarial noise. Extensive experiments across various benchmark datasets and continual learners validate our continual backdoor framework, achieving up to $100%$ attack success rate, with further ablation studies confirming our contributions' effectiveness.
|
http://arxiv.org/pdf/2406.19753v1
|
[
"Trang Nguyen",
"Anh Tran",
"Nhat Ho"
] |
2024-06-28T08:53:33Z
|
2024-06-28T08:53:33Z
|
2402.13914
|
Position: Explain to Question not to Justify
|
Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.
|
http://arxiv.org/pdf/2402.13914v2
|
[
"Przemyslaw Biecek",
"Wojciech Samek"
] |
2024-06-28T08:37:28Z
|
2024-02-21T16:30:24Z
|
2312.06560
|
Automatic Regularization for Linear MMSE Filters
|
In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the relationship with statistical machine learning methods, using a Bayesian approach, the regularization parameter is found from the observed signals in a simple and automatic manner. The proposed approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results.
|
http://arxiv.org/pdf/2312.06560v2
|
[
"Daniel Gomes de Pinho Zanco",
"Leszek Szczecinski",
"Jacob Benesty"
] |
2024-06-28T08:35:58Z
|
2023-12-11T17:45:10Z
|
2406.19738
|
Classical Bandit Algorithms for Entanglement Detection in Parameterized
Qubit States
|
Entanglement is a key resource for a wide range of tasks in quantum information and computing. Thus, verifying availability of this quantum resource is essential. Extensive research on entanglement detection has led to no-go theorems (Lu et al. [Phys. Rev. Lett., 116, 230501 (2016)]) that highlight the need for full state tomography (FST) in the absence of adaptive or joint measurements. Recent advancements, as proposed by Zhu, Teo, and Englert [Phys. Rev. A, 81, 052339, 2010], introduce a single-parameter family of entanglement witness measurements which are capable of conclusively detecting certain entangled states and only resort to FST when all witness measurements are inconclusive. We find a variety of realistic noisy two-qubit quantum states $mathcal{F}$ that yield conclusive results under this witness family. We solve the problem of detecting entanglement among $K$ quantum states in $mathcal{F}$, of which $m$ states are entangled, with $m$ potentially unknown. We recognize a structural connection of this problem to the Bad Arm Identification problem in stochastic Multi-Armed Bandits (MAB). In contrast to existing quantum bandit frameworks, we establish a new correspondence tailored for entanglement detection and term it the $(m,K)$-quantum Multi-Armed Bandit. We implement two well-known MAB policies for arbitrary states derived from $mathcal{F}$, present theoretical guarantees on the measurement/sample complexity and demonstrate the practicality of the policies through numerical simulations. More broadly, this paper highlights the potential for employing classical machine learning techniques for quantum entanglement detection.
|
http://arxiv.org/pdf/2406.19738v1
|
[
"Bharati. K",
"Vikesh Siddhu",
"Krishna Jagannathan"
] |
2024-06-28T08:26:47Z
|
2024-06-28T08:26:47Z
|
2407.00131
|
RepAct: The Re-parameterizable Adaptive Activation Function
|
Addressing the imperative need for efficient artificial intelligence in IoT and edge computing, this study presents RepAct, a re-parameterizable adaptive activation function tailored for optimizing lightweight neural networks within the computational limitations of edge devices. By employing a multi-branch structure with learnable adaptive weights, RepAct enriches feature processing and enhances cross-layer interpretability. When evaluated on tasks such as image classification and object detection, RepAct notably surpassed conventional activation functions in lightweight networks, delivering up to a 7.92% accuracy boost on MobileNetV3-Small for the ImageNet100 dataset, while maintaining computational complexity on par with HardSwish. This innovative approach not only maximizes model parameter efficiency but also significantly improves the performance and understanding capabilities of lightweight neural networks, demonstrating its potential for real-time edge computing applications.
|
http://arxiv.org/pdf/2407.00131v1
|
[
"Xian Wu",
"Qingchuan Tao",
"Shuang Wang"
] |
2024-06-28T08:25:45Z
|
2024-06-28T08:25:45Z
|
2406.19736
|
MM-Instruct: Generated Visual Instructions for Large Multimodal Model
Alignment
|
This paper introduces MM-Instruct, a large-scale dataset of diverse and high-quality visual instruction data designed to enhance the instruction-following capabilities of large multimodal models (LMMs). While existing visual instruction datasets often focus on question-answering, they struggle to generalize to broader application scenarios such as creative writing, summarization, or image analysis. To address these limitations, we propose a novel approach to constructing MM-Instruct that leverages the strong instruction-following capabilities of existing LLMs to generate novel visual instruction data from large-scale but conventional image captioning datasets. MM-Instruct first leverages ChatGPT to automatically generate diverse instructions from a small set of seed instructions through augmenting and summarization. It then matches these instructions with images and uses an open-sourced large language model (LLM) to generate coherent answers to the instruction-image pairs. The LLM is grounded by the detailed text descriptions of images in the whole answer generation process to guarantee the alignment of the instruction data. Moreover, we introduce a benchmark based on the generated instruction data to evaluate the instruction-following capabilities of existing LMMs. We demonstrate the effectiveness of MM-Instruct by training a LLaVA-1.5 model on the generated data, denoted as LLaVA-Instruct, which exhibits significant improvements in instruction-following capabilities compared to LLaVA-1.5 models. The MM-Instruct dataset, benchmark, and pre-trained models are available at https://github.com/jihaonew/MM-Instruct.
|
http://arxiv.org/pdf/2406.19736v1
|
[
"Jihao Liu",
"Xin Huang",
"Jinliang Zheng",
"Boxiao Liu",
"Jia Wang",
"Osamu Yoshie",
"Yu Liu",
"Hongsheng Li"
] |
2024-06-28T08:25:27Z
|
2024-06-28T08:25:27Z
|
2307.10635
|
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities
of Large Language Models
|
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations. To systematically examine the reasoning capabilities required for solving complex scientific problems, we introduce an expansive benchmark suite SciBench for LLMs. SciBench contains a carefully curated dataset featuring a range of collegiate-level scientific problems from mathematics, chemistry, and physics domains. Based on the dataset, we conduct an in-depth benchmarking study of representative open-source and proprietary LLMs with various prompting strategies. The results reveal that the current LLMs fall short of delivering satisfactory performance, with the best overall score of merely 43.22%. Furthermore, through a detailed user study, we categorize the errors made by LLMs into ten problem-solving abilities. Our analysis indicates that no single prompting strategy significantly outperforms the others and some strategies that demonstrate improvements in certain problem-solving skills could result in declines in other skills. We envision that SciBench will catalyze further developments in the reasoning abilities of LLMs, thereby ultimately contributing to scientific research and discovery.
|
http://arxiv.org/pdf/2307.10635v3
|
[
"Xiaoxuan Wang",
"Ziniu Hu",
"Pan Lu",
"Yanqiao Zhu",
"Jieyu Zhang",
"Satyen Subramaniam",
"Arjun R. Loomba",
"Shichang Zhang",
"Yizhou Sun",
"Wei Wang"
] |
2024-06-28T08:24:13Z
|
2023-07-20T07:01:57Z
|
2402.08114
|
Active Preference Learning for Large Language Models
|
As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important. A key consideration for aligning these models is how to most effectively use human resources, or model resources in the case where LLMs themselves are used as oracles. Reinforcement learning from Human or AI preferences (RLHF/RLAIF) is the most prominent example of such a technique, but is complex and often unstable. Direct Preference Optimization (DPO) has recently been proposed as a simpler and more stable alternative. In this work, we develop an active learning strategy for DPO to make better use of preference labels. We propose a practical acquisition function for prompt/completion pairs based on the predictive entropy of the language model and a measure of certainty of the implicit preference model optimized by DPO. We demonstrate how our approach improves both the rate of learning and final performance of fine-tuning on pairwise preference data.
|
http://arxiv.org/pdf/2402.08114v2
|
[
"William Muldrew",
"Peter Hayes",
"Mingtian Zhang",
"David Barber"
] |
2024-06-28T08:22:01Z
|
2024-02-12T23:09:00Z
|
2406.19726
|
EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans
|
Monocular Human Pose Estimation (HPE) aims at determining the 3D positions of human joints from a single 2D image captured by a camera. However, a single 2D point in the image may correspond to multiple points in 3D space. Typically, the uniqueness of the 2D-3D relationship is approximated using an orthographic or weak-perspective camera model. In this study, instead of relying on approximations, we advocate for utilizing the full perspective camera model. This involves estimating camera parameters and establishing a precise, unambiguous 2D-3D relationship. To do so, we introduce the EPOCH framework, comprising two main components: the pose lifter network (LiftNet) and the pose regressor network (RegNet). LiftNet utilizes the full perspective camera model to precisely estimate the 3D pose in an unsupervised manner. It takes a 2D pose and camera parameters as inputs and produces the corresponding 3D pose estimation. These inputs are obtained from RegNet, which starts from a single image and provides estimates for the 2D pose and camera parameters. RegNet utilizes only 2D pose data as weak supervision. Internally, RegNet predicts a 3D pose, which is then projected to 2D using the estimated camera parameters. This process enables RegNet to establish the unambiguous 2D-3D relationship. Our experiments show that modeling the lifting as an unsupervised task with a camera in-the-loop results in better generalization to unseen data. We obtain state-of-the-art results for the 3D HPE on the Human3.6M and MPI-INF-3DHP datasets. Our code is available at: [Github link upon acceptance, see supplementary materials].
|
http://arxiv.org/pdf/2406.19726v1
|
[
"Nicola Garau",
"Giulia Martinelli",
"Niccolò Bisagno",
"Denis Tomè",
"Carsten Stoll"
] |
2024-06-28T08:16:54Z
|
2024-06-28T08:16:54Z
|
2406.19714
|
State Matching and Multiple References in Adaptive Active Automata
Learning
|
Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude.
|
http://arxiv.org/pdf/2406.19714v1
|
[
"Loes Kruger",
"Sebastian Junges",
"Jurriaan Rot"
] |
2024-06-28T07:56:35Z
|
2024-06-28T07:56:35Z
|
2406.01124
|
Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
|
Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug-and-play tool to elicit logic tree-based explanations from Large Language Models (LLMs) to provide customized insights into each observed event sequence. Built on the temporal point process model for events, our method employs the likelihood function as a score to evaluate generated logic trees. We propose an amortized Expectation-Maximization (EM) learning framework and treat the logic tree as latent variables. In the E-step, we evaluate the posterior distribution over the latent logic trees using an LLM prior and the likelihood of the observed event sequences. LLM provides a high-quality prior for the latent logic trees, however, since the posterior is built over a discrete combinatorial space, we cannot get the closed-form solution. We propose to generate logic tree samples from the posterior using a learnable GFlowNet, which is a diversity-seeking generator for structured discrete variables. The M-step employs the generated logic rules to approximate marginalization over the posterior, facilitating the learning of model parameters and refining the tunable LLM prior parameters. In the online setting, our locally built, lightweight model will iteratively extract the most relevant rules from LLMs for each sequence using only a few iterations. Empirical demonstrations showcase the promising performance and adaptability of our framework.
|
http://arxiv.org/pdf/2406.01124v3
|
[
"Zitao Song",
"Chao Yang",
"Chaojie Wang",
"Bo An",
"Shuang Li"
] |
2024-06-28T07:54:19Z
|
2024-06-03T09:10:42Z
|
2406.09904
|
QQQ: Quality Quattuor-Bit Quantization for Large Language Models
|
Quantization is a proven effective method for compressing large language models. Although popular techniques like W8A8 and W4A16 effectively maintain model performance, they often fail to concurrently speed up the prefill and decoding stages of inference. W4A8 is a promising strategy to accelerate both of them while usually leads to a significant performance degradation. To address these issues, we present QQQ, a Quality Quattuor-bit Quantization method with 4-bit weights and 8-bit activations. QQQ employs adaptive smoothing and Hessian-based compensation, significantly enhancing the performance of quantized models without extensive training. Furthermore, we meticulously engineer W4A8 GEMM kernels to increase inference speed. Our specialized per-channel W4A8 GEMM and per-group W4A8 GEMM achieve impressive speed increases of 3.67$times$ and 3.29 $times$ over FP16 GEMM. Our extensive experiments show that QQQ achieves performance on par with existing state-of-the-art LLM quantization methods while significantly accelerating inference, achieving speed boosts up to 2.24 $times$, 2.10$times$, and 1.25$times$ compared to FP16, W8A8, and W4A16, respectively.
|
http://arxiv.org/pdf/2406.09904v2
|
[
"Ying Zhang",
"Peng Zhang",
"Mincong Huang",
"Jingyang Xiang",
"Yujie Wang",
"Chao Wang",
"Yineng Zhang",
"Lei Yu",
"Chuan Liu",
"Wei Lin"
] |
2024-06-28T07:53:12Z
|
2024-06-14T10:23:45Z
|
2406.19711
|
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause
Analysis in Multimodal Microservice Systems
|
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness. Due to the complex service invocation paths and dependencies at enterprise-level microservice systems, it is challenging to locate the anomalies promptly during service invocations, thus causing intractable issues for normal system operations and maintenance. In this paper, we propose a Causal Heterogeneous grAph baSed framEwork for root cause analysis, namely CHASE, for microservice systems with multimodal data, including traces, logs, and system monitoring metrics. Specifically, related information is encoded into representative embeddings and further modeled by a multimodal invocation graph. Following that, anomaly detection is performed on each instance node with attentive heterogeneous message passing from its adjacent metric and log nodes. Finally, CHASE learns from the constructed hypergraph with hyperedges representing the flow of causality and performs root cause localization. We evaluate the proposed framework on two public microservice datasets with distinct attributes and compare with the state-of-the-art methods. The results show that CHASE achieves the average performance gain up to 36.2%(A@1) and 29.4%(Percentage@1), respectively to its best counterpart.
|
http://arxiv.org/pdf/2406.19711v1
|
[
"Ziming Zhao",
"Tiehua Zhang",
"Zhishu Shen",
"Hai Dong",
"Xingjun Ma",
"Xianhui Liu",
"Yun Yang"
] |
2024-06-28T07:46:51Z
|
2024-06-28T07:46:51Z
|
2406.19707
|
InfiniGen: Efficient Generative Inference of Large Language Models with
Dynamic KV Cache Management
|
Transformer-based large language models (LLMs) demonstrate impressive performance across various natural language processing tasks. Serving LLM inference for generating long contents, however, poses a challenge due to the enormous memory footprint of the transient state, known as the key-value (KV) cache, which scales with the sequence length and batch size. In this paper, we present InfiniGen, a novel KV cache management framework tailored for long-text generation, which synergistically works with modern offloading-based inference systems. InfiniGen leverages the key insight that a few important tokens that are essential for computing the subsequent attention layer in the Transformer can be speculated by performing a minimal rehearsal with the inputs of the current layer and part of the query weight and key cache of the subsequent layer. This allows us to prefetch only the essential KV cache entries (without fetching them all), thereby mitigating the fetch overhead from the host memory in offloading-based LLM serving systems. Our evaluation on several representative LLMs shows that InfiniGen improves the overall performance of a modern offloading-based system by up to 3.00x compared to prior KV cache management methods while offering substantially better model accuracy.
|
http://arxiv.org/pdf/2406.19707v1
|
[
"Wonbeom Lee",
"Jungi Lee",
"Junghwan Seo",
"Jaewoong Sim"
] |
2024-06-28T07:41:26Z
|
2024-06-28T07:41:26Z
|
2406.12569
|
MOYU: A Theoretical Study on Massive Over-activation Yielded Uplifts in
LLMs
|
Massive Over-activation Yielded Uplifts(MOYU) is an inherent property of large language models, and dynamic activation(DA) based on the MOYU property is a clever yet under-explored strategy designed to accelerate inference in these models. Existing methods that utilize MOYU often face a significant 'Impossible Trinity': struggling to simultaneously maintain model performance, enhance inference speed, and extend applicability across various architectures. Due to the theoretical ambiguities surrounding MOYU, this paper elucidates the root cause of the MOYU property and outlines the mechanisms behind two primary limitations encountered by current DA methods: 1) history-related activation uncertainty, and 2) semantic-irrelevant activation inertia. Our analysis not only underscores the limitations of current dynamic activation strategies within large-scale LLaMA models but also proposes opportunities for refining the design of future sparsity schemes.
|
http://arxiv.org/pdf/2406.12569v2
|
[
"Chi Ma",
"Mincong Huang",
"Chao Wang",
"Yujie Wang",
"Lei Yu"
] |
2024-06-28T07:23:16Z
|
2024-06-18T12:57:33Z
|
2402.11622
|
Logical Closed Loop: Uncovering Object Hallucinations in Large
Vision-Language Models
|
Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detection methods have been proposed, which either require large-scare computational resources or depend on the detection result of external models. However, there remains an under-explored field to utilize the LVLM itself to alleviate object hallucinations. In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects. Therefore, we propose a Logical Closed Loop-based framework for Object Hallucination Detection and Mitigation, namely LogicCheckGPT. In specific, we devise logical consistency probing to raise questions with logical correlations, inquiring about attributes from objects and vice versa. Whether their responses can form a logical closed loop serves as an indicator of object hallucination. As a plug-and-play method, it can be seamlessly applied to all existing LVLMs. Comprehensive experiments conducted on three benchmarks across four LVLMs have demonstrated significant improvements brought by our method, indicating its effectiveness and generality.
|
http://arxiv.org/pdf/2402.11622v2
|
[
"Junfei Wu",
"Qiang Liu",
"Ding Wang",
"Jinghao Zhang",
"Shu Wu",
"Liang Wang",
"Tieniu Tan"
] |
2024-06-28T07:20:22Z
|
2024-02-18T15:28:39Z
|
2401.11447
|
Sequential Model for Predicting Patient Adherence in Subcutaneous
Immunotherapy for Allergic Rhinitis
|
Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in three years SCIT. Methods: The research develops and analyzes two models, sequential latent-variable model (SLVM) of Sequential Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM) evaluating them based on scoring and adherence prediction capabilities. Results: Excluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60% to 72%, and for LSTM models, it is 66% to 84%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55. Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.
|
http://arxiv.org/pdf/2401.11447v4
|
[
"Yin Li",
"Yu Xiong",
"Wenxin Fan",
"Kai Wang",
"Qingqing Yu",
"Liping Si",
"Patrick van der Smagt",
"Jun Tang",
"Nutan Chen"
] |
2024-06-28T06:53:51Z
|
2024-01-21T09:55:47Z
|
2406.17963
|
Empowering Interdisciplinary Insights with Dynamic Graph Embedding
Trajectories
|
We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph (DTDG) models to adeptly handle the temporal dynamics inherent in dynamic graphs. DyGETViz effectively captures both micro- and macro-level structural shifts within these graphs, offering a robust method for representing complex and massive dynamic graphs. The application of DyGETViz extends to a diverse array of domains, including ethology, epidemiology, finance, genetics, linguistics, communication studies, social studies, and international relations. Through its implementation, DyGETViz has revealed or confirmed various critical insights. These include the diversity of content sharing patterns and the degree of specialization within online communities, the chronological evolution of lexicons across decades, and the distinct trajectories exhibited by aging-related and non-related genes. Importantly, DyGETViz enhances the accessibility of scientific findings to non-domain experts by simplifying the complexities of dynamic graphs. Our framework is released as an open-source Python package for use across diverse disciplines. Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.
|
http://arxiv.org/pdf/2406.17963v2
|
[
"Yiqiao Jin",
"Andrew Zhao",
"Yeon-Chang Lee",
"Meng Ye",
"Ajay Divakaran",
"Srijan Kumar"
] |
2024-06-28T06:44:45Z
|
2024-06-25T22:44:53Z
|
2406.19674
|
Less is More: Accurate Speech Recognition & Translation without
Web-Scale Data
|
Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data. We argue that state-of-the art accuracy can be reached without relying on web-scale data. Canary - multilingual ASR and speech translation model, outperforms current state-of-the-art models - Whisper, OWSM, and Seamless-M4T on English, French, Spanish, and German languages, while being trained on an order of magnitude less data than these models. Three key factors enables such data-efficient model: (1) a FastConformer-based attention encoder-decoder architecture (2) training on synthetic data generated with machine translation and (3) advanced training techniques: data-balancing, dynamic data blending, dynamic bucketing and noise-robust fine-tuning. The model, weights, and training code will be open-sourced.
|
http://arxiv.org/pdf/2406.19674v1
|
[
"Krishna C. Puvvada",
"Piotr Żelasko",
"He Huang",
"Oleksii Hrinchuk",
"Nithin Rao Koluguri",
"Kunal Dhawan",
"Somshubra Majumdar",
"Elena Rastorgueva",
"Zhehuai Chen",
"Vitaly Lavrukhin",
"Jagadeesh Balam",
"Boris Ginsburg"
] |
2024-06-28T06:22:23Z
|
2024-06-28T06:22:23Z
|
2406.17051
|
Leveraging Knowledge Distillation for Lightweight Skin Cancer
Classification: Balancing Accuracy and Computational Efficiency
|
Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective skin cancer classification, we address the limitations of existing models, which are often too large to deploy in areas with limited computational resources. In response, we present a knowledge distillation based approach for creating a lightweight yet high-performing classifier. The proposed solution involves fusing three models, namely ResNet152V2, ConvNeXtBase, and ViT Base, to create an effective teacher model. The teacher model is then employed to guide a lightweight student model of size 2.03 MB. This student model is further compressed to 469.77 KB using 16-bit quantization, enabling smooth incorporation into edge devices. With six-stage image preprocessing, data augmentation, and a rigorous ablation study, the model achieves an impressive accuracy of 98.75% on the HAM10000 dataset and 98.94% on the Kaggle dataset in classifying benign and malignant skin cancers. With its high accuracy and compact size, our model appears to be a potential choice for accurate skin cancer classification, particularly in resource-constrained settings.
|
http://arxiv.org/pdf/2406.17051v2
|
[
"Niful Islam",
"Khan Md Hasib",
"Fahmida Akter Joti",
"Asif Karim",
"Sami Azam"
] |
2024-06-28T05:50:11Z
|
2024-06-24T18:13:09Z
|
2406.19237
|
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with
Flowcharts
|
Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. FlowVQA comprises 2,272 carefully generated and human-verified flowchart images from three distinct content sources, along with 22,413 diverse question-answer pairs, to test a spectrum of reasoning tasks, including information localization, decision-making, and logical progression. We conduct a thorough baseline evaluation on a suite of both open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. The results underscore the benchmark's potential as a vital tool for advancing the field of multimodal modeling, providing a focused and challenging environment for enhancing model performance in visual and logical reasoning tasks.
|
http://arxiv.org/pdf/2406.19237v2
|
[
"Shubhankar Singh",
"Purvi Chaurasia",
"Yerram Varun",
"Pranshu Pandya",
"Vatsal Gupta",
"Vivek Gupta",
"Dan Roth"
] |
2024-06-28T05:43:46Z
|
2024-06-27T15:01:48Z
|
2406.11741
|
Transcendence: Generative Models Can Outperform The Experts That Train
Them
|
Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by humans, we may not expect the artificial model to outperform the humans on their original objectives. In this work, we study the phenomenon of transcendence: when a generative model achieves capabilities that surpass the abilities of the experts generating its data. We demonstrate transcendence by training an autoregressive transformer to play chess from game transcripts, and show that the trained model can sometimes achieve better performance than all players in the dataset. We theoretically prove that transcendence can be enabled by low-temperature sampling, and rigorously assess this claim experimentally. Finally, we discuss other sources of transcendence, laying the groundwork for future investigation of this phenomenon in a broader setting.
|
http://arxiv.org/pdf/2406.11741v3
|
[
"Edwin Zhang",
"Vincent Zhu",
"Naomi Saphra",
"Anat Kleiman",
"Benjamin L. Edelman",
"Milind Tambe",
"Sham M. Kakade",
"Eran Malach"
] |
2024-06-28T05:28:27Z
|
2024-06-17T17:00:52Z
|
2406.19662
|
Finite basis Kolmogorov-Arnold networks: domain decomposition for
data-driven and physics-informed problems
|
Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be expensive to train, even for relatively small networks. Inspired by finite basis physics-informed neural networks (FBPINNs), in this work, we develop a domain decomposition method for KANs that allows for several small KANs to be trained in parallel to give accurate solutions for multiscale problems. We show that finite basis KANs (FBKANs) can provide accurate results with noisy data and for physics-informed training.
|
http://arxiv.org/pdf/2406.19662v1
|
[
"Amanda A. Howard",
"Bruno Jacob",
"Sarah H. Murphy",
"Alexander Heinlein",
"Panos Stinis"
] |
2024-06-28T05:13:43Z
|
2024-06-28T05:13:43Z
|
2406.19653
|
ACES: Automatic Cohort Extraction System for Event-Stream Datasets
|
Reproducibility remains a significant challenge in machine learning (ML) for healthcare. In this field, datasets, model pipelines, and even task/cohort definitions are often private, leading to a significant barrier in sharing, iterating, and understanding ML results on electronic health record (EHR) datasets. In this paper, we address a significant part of this problem by introducing the Automatic Cohort Extraction System for Event-Stream Datasets (ACES). This tool is designed to simultaneously simplify the development of task/cohorts for ML in healthcare and enable the reproduction of these cohorts, both at an exact level for single datasets and at a conceptual level across datasets. To accomplish this, ACES provides (1) a highly intuitive and expressive configuration language for defining both dataset-specific concepts and dataset-agnostic inclusion/exclusion criteria, and (2) a pipeline to automatically extract patient records that meet these defined criteria from real-world data. ACES can be automatically applied to any dataset in either the Medical Event Data Standard (MEDS) or EventStreamGPT (ESGPT) formats, or to *any* dataset for which the necessary task-specific predicates can be extracted in an event-stream form. ACES has the potential to significantly lower the barrier to entry for defining ML tasks, redefine the way researchers interact with EHR datasets, and significantly improve the state of reproducibility for ML studies in this modality. ACES is available at https://github.com/justin13601/aces.
|
http://arxiv.org/pdf/2406.19653v1
|
[
"Justin Xu",
"Jack Gallifant",
"Alistair E. W. Johnson",
"Matthew B. A. McDermott"
] |
2024-06-28T04:48:05Z
|
2024-06-28T04:48:05Z
|
2403.03181
|
Behavior Generation with Latent Actions
|
Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction. A recent class of models called Behavior Transformers (BeT) addresses this by discretizing actions using k-means clustering to capture different modes. However, k-means struggles to scale for high-dimensional action spaces or long sequences, and lacks gradient information, and thus BeT suffers in modeling long-range actions. In this work, we present Vector-Quantized Behavior Transformer (VQ-BeT), a versatile model for behavior generation that handles multimodal action prediction, conditional generation, and partial observations. VQ-BeT augments BeT by tokenizing continuous actions with a hierarchical vector quantization module. Across seven environments including simulated manipulation, autonomous driving, and robotics, VQ-BeT improves on state-of-the-art models such as BeT and Diffusion Policies. Importantly, we demonstrate VQ-BeT's improved ability to capture behavior modes while accelerating inference speed 5x over Diffusion Policies. Videos and code can be found https://sjlee.cc/vq-bet
|
http://arxiv.org/pdf/2403.03181v2
|
[
"Seungjae Lee",
"Yibin Wang",
"Haritheja Etukuru",
"H. Jin Kim",
"Nur Muhammad Mahi Shafiullah",
"Lerrel Pinto"
] |
2024-06-28T04:15:33Z
|
2024-03-05T18:19:29Z
|
2406.19642
|
IDT: Dual-Task Adversarial Attacks for Privacy Protection
|
Natural language processing (NLP) models may leak private information in different ways, including membership inference, reconstruction or attribute inference attacks. Sensitive information may not be explicit in the text, but hidden in underlying writing characteristics. Methods to protect privacy can involve using representations inside models that are demonstrated not to detect sensitive attributes or -- for instance, in cases where users might not trust a model, the sort of scenario of interest here -- changing the raw text before models can have access to it. The goal is to rewrite text to prevent someone from inferring a sensitive attribute (e.g. the gender of the author, or their location by the writing style) whilst keeping the text useful for its original intention (e.g. the sentiment of a product review). The few works tackling this have focused on generative techniques. However, these often create extensively different texts from the original ones or face problems such as mode collapse. This paper explores a novel adaptation of adversarial attack techniques to manipulate a text to deceive a classifier w.r.t one task (privacy) whilst keeping the predictions of another classifier trained for another task (utility) unchanged. We propose IDT, a method that analyses predictions made by auxiliary and interpretable models to identify which tokens are important to change for the privacy task, and which ones should be kept for the utility task. We evaluate different datasets for NLP suitable for different tasks. Automatic and human evaluations show that IDT retains the utility of text, while also outperforming existing methods when deceiving a classifier w.r.t privacy task.
|
http://arxiv.org/pdf/2406.19642v1
|
[
"Pedro Faustini",
"Shakila Mahjabin Tonni",
"Annabelle McIver",
"Qiongkai Xu",
"Mark Dras"
] |
2024-06-28T04:14:35Z
|
2024-06-28T04:14:35Z
|
2406.02105
|
Kernel vs. Kernel: Exploring How the Data Structure Affects Neural
Collapse
|
Recently, a vast amount of literature has focused on the "Neural Collapse" (NC) phenomenon, which emerges when training neural network (NN) classifiers beyond the zero training error point. The core component of NC is the decrease in the within class variability of the network's deepest features, dubbed as NC1. The theoretical works that study NC are typically based on simplified unconstrained features models (UFMs) that mask any effect of the data on the extent of collapse. In this paper, we provide a kernel-based analysis that does not suffer from this limitation. First, given a kernel function, we establish expressions for the traces of the within- and between-class covariance matrices of the samples' features (and consequently an NC1 metric). Then, we turn to focus on kernels associated with shallow NNs. First, we consider the NN Gaussian Process kernel (NNGP), associated with the network at initialization, and the complement Neural Tangent Kernel (NTK), associated with its training in the "lazy regime". Interestingly, we show that the NTK does not represent more collapsed features than the NNGP for prototypical data models. As NC emerges from training, we then consider an alternative to NTK: the recently proposed adaptive kernel, which generalizes NNGP to model the feature mapping learned from the training data. Contrasting our NC1 analysis for these two kernels enables gaining insights into the effect of data distribution on the extent of collapse, which are empirically aligned with the behavior observed with practical training of NNs.
|
http://arxiv.org/pdf/2406.02105v2
|
[
"Vignesh Kothapalli",
"Tom Tirer"
] |
2024-06-28T04:05:53Z
|
2024-06-04T08:33:56Z
|
2405.16141
|
AIGB: Generative Auto-bidding via Diffusion Modeling
|
Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding. However, most current RL auto-bidding methods are modeled through the Markovian Decision Process (MDP), which assumes the Markovian state transition. This assumption restricts the ability to perform in long horizon scenarios and makes the model unstable when dealing with highly random online advertising environments. To tackle this issue, this paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. DiffBid directly models the correlation between the return and the entire trajectory, effectively avoiding error propagation across time steps in long horizons. Additionally, DiffBid offers a versatile approach for generating trajectories that maximize given targets while adhering to specific constraints. Extensive experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid, achieving 2.81% increase in GMV and 3.36% increase in ROI.
|
http://arxiv.org/pdf/2405.16141v3
|
[
"Jiayan Guo",
"Yusen Huo",
"Zhilin Zhang",
"Tianyu Wang",
"Chuan Yu",
"Jian Xu",
"Yan Zhang",
"Bo Zheng"
] |
2024-06-28T03:59:15Z
|
2024-05-25T09:21:43Z
|
2301.12616
|
Active Sequential Two-Sample Testing
|
A two-sample hypothesis test is a statistical procedure used to determine whether the distributions generating two samples are identical. We consider the two-sample testing problem in a new scenario where the sample measurements (or sample features) are inexpensive to access, but their group memberships (or labels) are costly. To address the problem, we devise the first emph{active sequential two-sample testing framework} that not only sequentially but also emph{actively queries}. Our test statistic is a likelihood ratio where one likelihood is found by maximization over all class priors, and the other is provided by a probabilistic classification model. The classification model is adaptively updated and used to predict where the (unlabelled) features have a high dependency on labels; labeling the ``high-dependency'' features leads to the increased power of the proposed testing framework. In theory, we provide the proof that our framework produces an emph{anytime-valid} $p$-value. In addition, we characterize the proposed framework's gain in testing power by analyzing the mutual information between the feature and label variables in asymptotic and finite-sample scenarios. In practice, we introduce an instantiation of our framework and evaluate it using several experiments; the experiments on the synthetic, MNIST, and application-specific datasets demonstrate that the testing power of the instantiated active sequential test significantly increases while the Type I error is under control.
|
http://arxiv.org/pdf/2301.12616v4
|
[
"Weizhi Li",
"Prad Kadambi",
"Pouria Saidi",
"Karthikeyan Natesan Ramamurthy",
"Gautam Dasarathy",
"Visar Berisha"
] |
2024-06-28T03:57:21Z
|
2023-01-30T02:23:49Z
|
2310.01712
|
Generative Autoencoding of Dropout Patterns
|
We propose a generative model termed Deciphering Autoencoders. In this model, we assign a unique random dropout pattern to each data point in the training dataset and then train an autoencoder to reconstruct the corresponding data point using this pattern as information to be encoded. Even if a completely random dropout pattern is assigned to each data point regardless of their similarities, a sufficiently large encoder can smoothly map them to a low-dimensional latent space to reconstruct individual training data points. During inference, using a dropout pattern different from those used during training allows the model to function as a generator. Since the training of Deciphering Autoencoders relies solely on reconstruction error, it offers more stable training compared to other generative models. Despite their simplicity, Deciphering Autoencoders show sampling quality comparable to DCGAN on the CIFAR-10 dataset.
|
http://arxiv.org/pdf/2310.01712v2
|
[
"Shunta Maeda"
] |
2024-06-28T03:53:56Z
|
2023-10-03T00:54:13Z
|
2406.04607
|
MeGA: Merging Multiple Independently Trained Neural Networks Based on
Genetic Algorithm
|
In this paper, we introduce a novel method for merging the weights of multiple pre-trained neural networks using a genetic algorithm called MeGA. Traditional techniques, such as weight averaging and ensemble methods, often fail to fully harness the capabilities of pre-trained networks. Our approach leverages a genetic algorithm with tournament selection, crossover, and mutation to optimize weight combinations, creating a more effective fusion. This technique allows the merged model to inherit advantageous features from both parent models, resulting in enhanced accuracy and robustness. Through experiments on the CIFAR-10 dataset, we demonstrate that our genetic algorithm-based weight merging method improves test accuracy compared to individual models and conventional methods. This approach provides a scalable solution for integrating multiple pre-trained networks across various deep learning applications. Github is available at: https://github.com/YUNBLAK/MeGA-Merging-Multiple-Independently-Trained-Neural-Networks-Based-on-Genetic-Algorithm
|
http://arxiv.org/pdf/2406.04607v4
|
[
"Daniel Yun"
] |
2024-06-28T03:53:21Z
|
2024-06-07T03:31:58Z
|
2407.00128
|
When Search Engine Services meet Large Language Models: Visions and
Challenges
|
Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we search for and retrieve information, understand content, and interact with internet services. This paper conducts an in-depth examination of how integrating LLMs with search engines can mutually benefit both technologies. We focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). For Search4LLM, we investigate how search engines can provide diverse high-quality datasets for pre-training of LLMs, how they can use the most relevant documents to help LLMs learn to answer queries more accurately, how training LLMs with Learning-To-Rank (LTR) tasks can enhance their ability to respond with greater precision, and how incorporating recent search results can make LLM-generated content more accurate and current. In terms of LLM4Search, we examine how LLMs can be used to summarize content for better indexing by search engines, improve query outcomes through optimization, enhance the ranking of search results by analyzing document relevance, and help in annotating data for learning-to-rank tasks in various learning contexts. However, this promising integration comes with its challenges, which include addressing potential biases and ethical issues in training models, managing the computational and other costs of incorporating LLMs into search services, and continuously updating LLM training with the ever-changing web content. We discuss these challenges and chart out required research directions to address them. We also discuss broader implications for service computing, such as scalability, privacy concerns, and the need to adapt search engine architectures for these advanced models.
|
http://arxiv.org/pdf/2407.00128v1
|
[
"Haoyi Xiong",
"Jiang Bian",
"Yuchen Li",
"Xuhong Li",
"Mengnan Du",
"Shuaiqiang Wang",
"Dawei Yin",
"Sumi Helal"
] |
2024-06-28T03:52:13Z
|
2024-06-28T03:52:13Z
|
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