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2406.03229
|
Global Clipper: Enhancing Safety and Reliability of Transformer-based
Object Detection Models
|
As transformer-based object detection models progress, their impact in critical sectors like autonomous vehicles and aviation is expected to grow. Soft errors causing bit flips during inference have significantly impacted DNN performance, altering predictions. Traditional range restriction solutions for CNNs fall short for transformers. This study introduces the Global Clipper and Global Hybrid Clipper, effective mitigation strategies specifically designed for transformer-based models. It significantly enhances their resilience to soft errors and reduces faulty inferences to ~ 0%. We also detail extensive testing across over 64 scenarios involving two transformer models (DINO-DETR and Lite-DETR) and two CNN models (YOLOv3 and SSD) using three datasets, totalling approximately 3.3 million inferences, to assess model robustness comprehensively. Moreover, the paper explores unique aspects of attention blocks in transformers and their operational differences from CNNs.
|
http://arxiv.org/pdf/2406.03229v4
|
[
"Qutub Syed Sha",
"Michael Paulitsch",
"Karthik Pattabiraman",
"Korbinian Hagn",
"Fabian Oboril",
"Cornelius Buerkle",
"Kay-Ulrich Scholl",
"Gereon Hinz",
"Alois Knoll"
] |
2024-07-09T10:23:53Z
|
2024-06-05T13:06:17Z
|
2407.06723
|
Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting
Region Captions
|
Humans describe complex scenes with compositionality, using simple text descriptions enriched with links and relationships. While vision-language research has aimed to develop models with compositional understanding capabilities, this is not reflected yet in existing datasets which, for the most part, still use plain text to describe images. In this work, we propose a new annotation strategy, graph-based captioning (GBC) that describes an image using a labelled graph structure, with nodes of various types. The nodes in GBC are created using, in a first stage, object detection and dense captioning tools nested recursively to uncover and describe entity nodes, further linked together in a second stage by highlighting, using new types of nodes, compositions and relations among entities. Since all GBC nodes hold plain text descriptions, GBC retains the flexibility found in natural language, but can also encode hierarchical information in its edges. We demonstrate that GBC can be produced automatically, using off-the-shelf multimodal LLMs and open-vocabulary detection models, by building a new dataset, GBC10M, gathering GBC annotations for about 10M images of the CC12M dataset. We use GBC10M to showcase the wealth of node captions uncovered by GBC, as measured with CLIP training. We show that using GBC nodes' annotations -- notably those stored in composition and relation nodes -- results in significant performance boost on downstream models when compared to other dataset formats. To further explore the opportunities provided by GBC, we also propose a new attention mechanism that can leverage the entire GBC graph, with encouraging experimental results that show the extra benefits of incorporating the graph structure. Our datasets are released at url{https://huggingface.co/graph-based-captions}.
|
http://arxiv.org/pdf/2407.06723v1
|
[
"Yu-Guan Hsieh",
"Cheng-Yu Hsieh",
"Shih-Ying Yeh",
"Louis Béthune",
"Hadi Pour Ansari",
"Pavan Kumar Anasosalu Vasu",
"Chun-Liang Li",
"Ranjay Krishna",
"Oncel Tuzel",
"Marco Cuturi"
] |
2024-07-09T09:55:04Z
|
2024-07-09T09:55:04Z
|
2404.08271
|
Transfer Learning Study of Motion Transformer-based Trajectory
Predictions
|
Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive results in simulation-based challenges, with transformer-based architectures technologically leading the way. Ultimately, however, predictions are needed in the real world. In addition to the shifts from simulation to the real world, many vehicle- and country-specific shifts, i.e. differences in sensor systems, fusion and perception algorithms as well as traffic rules and laws, are on the agenda. Since models that can cover all system setups and design domains at once are not yet foreseeable, model adaptation plays a central role. Therefore, a simulation-based study on transfer learning techniques is conducted on basis of a transformer-based model. Furthermore, the study aims to provide insights into possible trade-offs between computational time and performance to support effective transfers into the real world.
|
http://arxiv.org/pdf/2404.08271v2
|
[
"Lars Ullrich",
"Alex McMaster",
"Knut Graichen"
] |
2024-07-09T09:46:06Z
|
2024-04-12T06:50:32Z
|
2407.05193
|
CBM: Curriculum by Masking
|
We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum learning strategy that effectively creates an easy-to-hard training schedule via patch (token) masking, offering significant accuracy improvements over the conventional training regime and previous curriculum learning (CL) methods. CBM leverages gradient magnitudes to prioritize the masking of salient image regions via a novel masking algorithm and a novel masking block. Our approach enables controlling sample difficulty via the patch masking ratio, generating an effective easy-to-hard curriculum by gradually introducing harder samples as training progresses. CBM operates with two easily configurable parameters, i.e. the number of patches and the curriculum schedule, making it a versatile curriculum learning approach for object recognition and detection. We conduct experiments with various neural architectures, ranging from convolutional networks to vision transformers, on five benchmark data sets (CIFAR-10, CIFAR-100, ImageNet, Food-101 and PASCAL VOC), to compare CBM with conventional as well as curriculum-based training regimes. Our results reveal the superiority of our strategy compared with the state-of-the-art curriculum learning regimes. We also observe improvements in transfer learning contexts, where CBM surpasses previous work by considerable margins in terms of accuracy. We release our code for free non-commercial use at https://github.com/CroitoruAlin/CBM.
|
http://arxiv.org/pdf/2407.05193v2
|
[
"Andrei Jarca",
"Florinel-Alin Croitoru",
"Radu Tudor Ionescu"
] |
2024-07-09T09:40:38Z
|
2024-07-06T21:35:18Z
|
2407.06712
|
MDP Geometry, Normalization and Value Free Solvers
|
Markov Decision Process (MDP) is a common mathematical model for sequential decision-making problems. In this paper, we present a new geometric interpretation of MDP, which is useful for analyzing the dynamics of main MDP algorithms. Based on this interpretation, we demonstrate that MDPs can be split into equivalence classes with indistinguishable algorithm dynamics. The related normalization procedure allows for the design of a new class of MDP-solving algorithms that find optimal policies without computing policy values.
|
http://arxiv.org/pdf/2407.06712v1
|
[
"Arsenii Mustafin",
"Aleksei Pakharev",
"Alex Olshevsky",
"Ioannis Ch. Paschalidis"
] |
2024-07-09T09:39:45Z
|
2024-07-09T09:39:45Z
|
2407.06709
|
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures
for Multi-Label Classification
|
Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have proposed various measures to evaluate model performances. However, both theoretical analysis and empirical observations show that a model might perform inconsistently on different measures. To bridge this gap, this paper proposes a novel measure named Top-K Pairwise Ranking (TKPR), and a series of analyses show that TKPR is compatible with existing ranking-based measures. In light of this, we further establish an empirical surrogate risk minimization framework for TKPR. On one hand, the proposed framework enjoys convex surrogate losses with the theoretical support of Fisher consistency. On the other hand, we establish a sharp generalization bound for the proposed framework based on a novel technique named data-dependent contraction. Finally, empirical results on benchmark datasets validate the effectiveness of the proposed framework.
|
http://arxiv.org/abs/2407.06709v1
|
[
"Zitai Wang",
"Qianqian Xu",
"Zhiyong Yang",
"Peisong Wen",
"Yuan He",
"Xiaochun Cao",
"Qingming Huang"
] |
2024-07-09T09:36:37Z
|
2024-07-09T09:36:37Z
|
2403.08040
|
MicroT: Low-Energy and Adaptive Models for MCUs
|
We propose MicroT, a low-energy, multi-task adaptive model framework for resource-constrained MCUs. We divide the original model into a feature extractor and a classifier. The feature extractor is obtained through self-supervised knowledge distillation and further optimized into part and full models through model splitting and joint training. These models are then deployed on MCUs, with classifiers added and trained on local tasks, ultimately performing stage-decision for joint inference. In this process, the part model initially processes the sample, and if the confidence score falls below the set threshold, the full model will resume and continue the inference. We evaluate MicroT on two models, three datasets, and two MCU boards. Our experimental evaluation shows that MicroT effectively improves model performance and reduces energy consumption when dealing with multiple local tasks. Compared to the unoptimized feature extractor, MicroT can improve accuracy by up to 9.87%. On MCUs, compared to the standard full model inference, MicroT can save up to about 29.13% in energy consumption. MicroT also allows users to adaptively adjust the stage-decision ratio as needed, better balancing model performance and energy consumption. Under the standard stage-decision ratio configuration, MicroT can increase accuracy by 5.91% and save about 14.47% of energy consumption.
|
http://arxiv.org/pdf/2403.08040v2
|
[
"Yushan Huang",
"Ranya Aloufi",
"Xavier Cadet",
"Yuchen Zhao",
"Payam Barnaghi",
"Hamed Haddadi"
] |
2024-07-09T09:33:45Z
|
2024-03-12T19:23:13Z
|
2402.15020
|
Probabilistically-Sound Beam Search with Masked Language Models
|
Beam search with masked language models (MLMs) is challenging in part because joint probability distributions over sequences are not readily available, unlike for autoregressive models. However, estimating such distributions has important domain-specific applications such as ancient text restoration and protein engineering. Here we present probabilistically-sound methods for beam search with MLMs. First, we clarify the conditions under which it is theoretically sound to perform text infilling with MLMs using standard beam search. When these conditions fail, we provide a probabilistically-sound modification with no additional computational complexity and demonstrate that it is superior to the aforementioned beam search in the expected conditions. We then present empirical results comparing several infilling approaches with MLMs across several domains.
|
http://arxiv.org/pdf/2402.15020v2
|
[
"Creston Brooks",
"Robert Calef",
"Charlie Cowen-Breen",
"Anna Sappington"
] |
2024-07-09T09:32:52Z
|
2024-02-22T23:36:26Z
|
2407.06704
|
Self-supervised visual learning from interactions with objects
|
Self-supervised learning (SSL) has revolutionized visual representation learning, but has not achieved the robustness of human vision. A reason for this could be that SSL does not leverage all the data available to humans during learning. When learning about an object, humans often purposefully turn or move around objects and research suggests that these interactions can substantially enhance their learning. Here we explore whether such object-related actions can boost SSL. For this, we extract the actions performed to change from one ego-centric view of an object to another in four video datasets. We then introduce a new loss function to learn visual and action embeddings by aligning the performed action with the representations of two images extracted from the same clip. This permits the performed actions to structure the latent visual representation. Our experiments show that our method consistently outperforms previous methods on downstream category recognition. In our analysis, we find that the observed improvement is associated with a better viewpoint-wise alignment of different objects from the same category. Overall, our work demonstrates that embodied interactions with objects can improve SSL of object categories.
|
http://arxiv.org/pdf/2407.06704v1
|
[
"Arthur Aubret",
"Céline Teulière",
"Jochen Triesch"
] |
2024-07-09T09:31:15Z
|
2024-07-09T09:31:15Z
|
2407.06703
|
HERMES: Holographic Equivariant neuRal network model for Mutational
Effect and Stability prediction
|
Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutational effect and stability prediction. Pre-trained to predict amino acid propensity from its surrounding 3D structure, HERMES can be fine-tuned for mutational effects using our open-source code. We present a suite of HERMES models, pre-trained with different strategies, and fine-tuned to predict the stability effect of mutations. Benchmarking against other models shows that HERMES often outperforms or matches their performance in predicting mutational effect on stability, binding, and fitness. HERMES offers versatile tools for evaluating mutational effects and can be fine-tuned for specific predictive objectives.
|
http://arxiv.org/pdf/2407.06703v1
|
[
"Gian Marco Visani",
"Michael N. Pun",
"William Galvin",
"Eric Daniel",
"Kevin Borisiak",
"Utheri Wagura",
"Armita Nourmohammad"
] |
2024-07-09T09:31:05Z
|
2024-07-09T09:31:05Z
|
2402.05966
|
Rethinking Model Re-Basin and Linear Mode Connectivity
|
Recent studies suggest that with sufficiently wide models, most SGD solutions can, up to permutation, converge into the same basin. This phenomenon, known as the model re-basin regime, has significant implications for model averaging by ensuring the linear mode connectivity. However, current re-basin strategies are ineffective in many scenarios due to a lack of comprehensive understanding of underlying mechanisms. Addressing this gap, this paper provides novel insights into understanding and improving the standard practice. Firstly, we decompose re-normalization into rescaling and reshift, uncovering that rescaling plays a crucial role in re-normalization while re-basin performance is sensitive to shifts in model activation. The finding calls for a more nuanced handling of the activation shift. Secondly, we identify that the merged model suffers from the issue of activation collapse and magnitude collapse. Varying the learning rate, weight decay, and initialization method can mitigate the issues and improve model performance. Lastly, we propose a new perspective to unify the re-basin and pruning, under which a lightweight yet effective post-pruning technique is derived, which can significantly improve the model performance after pruning. Our implementation is available at https://github.com/XingyuQu/rethink-re-basin.
|
http://arxiv.org/pdf/2402.05966v2
|
[
"Xingyu Qu",
"Samuel Horvath"
] |
2024-07-09T09:23:25Z
|
2024-02-05T17:06:26Z
|
2407.06698
|
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo
Supervision
|
Positive and Unlabeled (PU) learning, a binary classification model trained with only positive and unlabeled data, generally suffers from overfitted risk estimation due to inconsistent data distributions. To address this, we introduce a pseudo-supervised PU learning framework (PSPU), in which we train the PU model first, use it to gather confident samples for the pseudo supervision, and then apply these supervision to correct the PU model's weights by leveraging non-PU objectives. We also incorporate an additional consistency loss to mitigate noisy sample effects. Our PSPU outperforms recent PU learning methods significantly on MNIST, CIFAR-10, CIFAR-100 in both balanced and imbalanced settings, and enjoys competitive performance on MVTecAD for industrial anomaly detection.
|
http://arxiv.org/pdf/2407.06698v1
|
[
"Chengjie Wang",
"Chengming Xu",
"Zhenye Gan",
"Jianlong Hu",
"Wenbing Zhu",
"Lizhuag Ma"
] |
2024-07-09T09:19:01Z
|
2024-07-09T09:19:01Z
|
2407.06697
|
Certified Continual Learning for Neural Network Regression
|
On the one hand, there has been considerable progress on neural network verification in recent years, which makes certifying neural networks a possibility. On the other hand, neural networks in practice are often re-trained over time to cope with new data distribution or for solving different tasks (a.k.a. continual learning). Once re-trained, the verified correctness of the neural network is likely broken, particularly in the presence of the phenomenon known as catastrophic forgetting. In this work, we propose an approach called certified continual learning which improves existing continual learning methods by preserving, as long as possible, the established correctness properties of a verified network. Our approach is evaluated with multiple neural networks and on two different continual learning methods. The results show that our approach is efficient and the trained models preserve their certified correctness and often maintain high utility.
|
http://arxiv.org/pdf/2407.06697v1
|
[
"Long H. Pham",
"Jun Sun"
] |
2024-07-09T09:14:45Z
|
2024-07-09T09:14:45Z
|
2407.06690
|
Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
|
We introduce a novel approach to hierarchical reinforcement learning for Linearly-solvable Markov Decision Processes (LMDPs) in the infinite-horizon average-reward setting. Unlike previous work, our approach allows learning low-level and high-level tasks simultaneously, without imposing limiting restrictions on the low-level tasks. Our method relies on partitions of the state space that create smaller subtasks that are easier to solve, and the equivalence between such partitions to learn more efficiently. We then exploit the compositionality of low-level tasks to exactly represent the value function of the high-level task. Experiments show that our approach can outperform flat average-reward reinforcement learning by one or several orders of magnitude.
|
http://arxiv.org/pdf/2407.06690v1
|
[
"Guillermo Infante",
"Anders Jonsson",
"Vicenç Gómez"
] |
2024-07-09T09:06:44Z
|
2024-07-09T09:06:44Z
|
2312.04752
|
A Test-Time Learning Approach to Reparameterize the Geophysical Inverse
Problem with a Convolutional Neural Network
|
Regularization is critical for solving ill-posed geophysical inverse problems. Explicit regularization is often used, but there are opportunities to explore the implicit regularization effects that are inherent in a Neural Network structure. Researchers have discovered that the Convolutional Neural Network (CNN) architecture inherently enforces a regularization that is advantageous for addressing diverse inverse problems in computer vision, including de-noising and in-painting. In this study, we examine the applicability of this implicit regularization to geophysical inversions. The CNN maps an arbitrary vector to the model space. The predicted subsurface model is then fed into a forward numerical simulation to generate corresponding predicted measurements. Subsequently, the objective function value is computed by comparing these predicted measurements with the observed measurements. The backpropagation algorithm is employed to update the trainable parameters of the CNN during the inversion. Note that the CNN in our proposed method does not require training before the inversion, rather, the CNN weights are estimated in the inversion process, hence this is a test-time learning (TTL) approach. In this study, we choose to focus on the Direct Current (DC) resistivity inverse problem, which is representative of typical Tikhonov-style geophysical inversions (e.g. gravity, electromagnetic, etc.), to test our hypothesis. The experimental results demonstrate that the implicit regularization can be useful in some DC resistivity inversions. We also provide a discussion of the potential sources of this implicit regularization introduced from the CNN architecture and discuss some practical guides for applying the proposed method to other geophysical methods.
|
http://arxiv.org/abs/2312.04752v2
|
[
"Anran Xu",
"Lindsey J. Heagy"
] |
2024-07-09T09:06:34Z
|
2023-12-07T23:53:30Z
|
2402.09288
|
EcoVal: An Efficient Data Valuation Framework for Machine Learning
|
Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall value of the data can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as a textit{production function}, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models. The code is available at underline{https://github.com/respai-lab/ecoval}.
|
http://arxiv.org/pdf/2402.09288v5
|
[
"Ayush K Tarun",
"Vikram S Chundawat",
"Murari Mandal",
"Hong Ming Tan",
"Bowei Chen",
"Mohan Kankanhalli"
] |
2024-07-09T08:59:44Z
|
2024-02-14T16:21:47Z
|
2407.06682
|
A Predictive Model Based on Transformer with Statistical Feature
Embedding in Manufacturing Sensor Dataset
|
In the manufacturing process, sensor data collected from equipment is crucial for building predictive models to manage processes and improve productivity. However, in the field, it is challenging to gather sufficient data to build robust models. This study proposes a novel predictive model based on the Transformer, utilizing statistical feature embedding and window positional encoding. Statistical features provide an effective representation of sensor data, and the embedding enables the Transformer to learn both time- and sensor-related information. Window positional encoding captures precise time details from the feature embedding. The model's performance is evaluated in two problems: fault detection and virtual metrology, showing superior results compared to baseline models. This improvement is attributed to the efficient use of parameters, which is particularly beneficial for sensor data that often has limited sample sizes. The results support the model's applicability across various manufacturing industries, demonstrating its potential for enhancing process management and yield.
|
http://arxiv.org/pdf/2407.06682v1
|
[
"Gyeong Taek Lee",
"Oh-Ran Kwon"
] |
2024-07-09T08:59:27Z
|
2024-07-09T08:59:27Z
|
2407.06683
|
Accelerating Online Mapping and Behavior Prediction via Direct BEV
Feature Attention
|
Understanding road geometry is a critical component of the autonomous vehicle (AV) stack. While high-definition (HD) maps can readily provide such information, they suffer from high labeling and maintenance costs. Accordingly, many recent works have proposed methods for estimating HD maps online from sensor data. The vast majority of recent approaches encode multi-camera observations into an intermediate representation, e.g., a bird's eye view (BEV) grid, and produce vector map elements via a decoder. While this architecture is performant, it decimates much of the information encoded in the intermediate representation, preventing downstream tasks (e.g., behavior prediction) from leveraging them. In this work, we propose exposing the rich internal features of online map estimation methods and show how they enable more tightly integrating online mapping with trajectory forecasting. In doing so, we find that directly accessing internal BEV features yields up to 73% faster inference speeds and up to 29% more accurate predictions on the real-world nuScenes dataset.
|
http://arxiv.org/pdf/2407.06683v1
|
[
"Xunjiang Gu",
"Guanyu Song",
"Igor Gilitschenski",
"Marco Pavone",
"Boris Ivanovic"
] |
2024-07-09T08:59:27Z
|
2024-07-09T08:59:27Z
|
2302.03534
|
On the Limitation and Experience Replay for GNNs in Continual Learning
|
Continual learning seeks to empower models to progressively acquire information from a sequence of tasks. This approach is crucial for many real-world systems, which are dynamic and evolve over time. Recent research has witnessed a surge in the exploration of Graph Neural Networks (GNN) in Node-wise Graph Continual Learning (NGCL), a practical yet challenging paradigm involving the continual training of a GNN on node-related tasks. Despite recent advancements in continual learning strategies for GNNs in NGCL, a thorough theoretical understanding, especially regarding its learnability, is lacking. Learnability concerns the existence of a learning algorithm that can produce a good candidate model from the hypothesis/weight space, which is crucial for model selection in NGCL development. This paper introduces the first theoretical exploration of the learnability of GNN in NGCL, revealing that learnability is heavily influenced by structural shifts due to the interconnected nature of graph data. Specifically, GNNs may not be viable for NGCL under significant structural changes, emphasizing the need to manage structural shifts. To mitigate the impact of structural shifts, we propose a novel experience replay method termed Structure-Evolution-Aware Experience Replay (SEA-ER). SEA-ER features an innovative experience selection strategy that capitalizes on the topological awareness of GNNs, alongside a unique replay strategy that employs structural alignment to effectively counter catastrophic forgetting and diminish the impact of structural shifts on GNNs in NGCL. Our extensive experiments validate our theoretical insights and the effectiveness of SEA-ER.
|
http://arxiv.org/pdf/2302.03534v2
|
[
"Junwei Su",
"Difan Zou",
"Chuan Wu"
] |
2024-07-09T08:34:43Z
|
2023-02-07T15:36:08Z
|
2404.12718
|
Improving Prediction Accuracy of Semantic Segmentation Methods Using
Convolutional Autoencoder Based Pre-processing Layers
|
In this paper, we propose a method to improve prediction accuracy of semantic segmentation methods as follows: (1) construct a neural network that has pre-processing layers based on a convolutional autoencoder ahead of a semantic segmentation network, and (2) train the entire network initialized by the weights of the pre-trained autoencoder. We applied this method to the fully convolutional network (FCN) and experimentally compared its prediction accuracy on the cityscapes dataset. The Mean IoU of the proposed target model with the He normal initialization is 18.7% higher than that of FCN with the He normal initialization. In addition, those of the modified models of the target model are significantly higher than that of FCN with the He normal initialization. The accuracy and loss curves during the training showed that these are resulting from the improvement of the generalization ability. All of these results provide strong evidence that the proposed method is significantly effective in improving the prediction accuracy of FCN. The proposed method has the following features: it is comparatively simple, whereas the effect on improving the generalization ability and prediction accuracy of FCN is significant; the increase in the number of parameters by using it is very small, and that in the computation time is substantially large. In principle, the proposed method can be applied to other semantic segmentation methods. For semantic segmentation, at present, there is no effective way to improve the prediction accuracy of existing methods. None have published a method which is the same as or similar to our method and none have used such a method in practice. Therefore, we believe that our method is useful in practice and worthy of being widely known and used.
|
http://arxiv.org/pdf/2404.12718v2
|
[
"Hisashi Shimodaira"
] |
2024-07-09T08:33:59Z
|
2024-04-19T08:58:53Z
|
2407.00002
|
Kermut: Composite kernel regression for protein variant effects
|
Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modelling mutation similarity, which obtains state-of-the-art performance for protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging. We hope that this will encourage future work in this promising direction.
|
http://arxiv.org/pdf/2407.00002v2
|
[
"Peter Mørch Groth",
"Mads Herbert Kerrn",
"Lars Olsen",
"Jesper Salomon",
"Wouter Boomsma"
] |
2024-07-09T08:28:57Z
|
2024-04-09T14:08:06Z
|
2407.02119
|
Cost-Effective Proxy Reward Model Construction with On-Policy and Active
Learning
|
Reinforcement learning with human feedback (RLHF), as a widely adopted approach in current large language model pipelines, is textit{bottlenecked by the size of human preference data}. While traditional methods rely on offline preference dataset constructions, recent approaches have shifted towards online settings, where a learner uses a small amount of labeled seed data and a large pool of unlabeled prompts to iteratively construct new preference data through self-generated responses and high-quality reward/preference feedback. However, most current online algorithms still focus on preference labeling during policy model updating with given feedback oracles, which incurs significant expert query costs. textit{We are the first to explore cost-effective proxy reward oracles construction strategies for further labeling preferences or rewards with extremely limited labeled data and expert query budgets}. Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries. Using these methods, we train a evaluation model with minimal expert-labeled data, which then effectively labels nine times more preference pairs for further RLHF training. For instance, our model using Direct Preference Optimization (DPO) gains around over 1% average improvement on AlpacaEval2, MMLU-5shot and MMLU-0shot, with only 1.7K query cost. Our methodology is orthogonal to other direct expert query-based strategies and therefore might be integrated with them to further reduce query costs.
|
http://arxiv.org/pdf/2407.02119v2
|
[
"Yifang Chen",
"Shuohang Wang",
"Ziyi Yang",
"Hiteshi Sharma",
"Nikos Karampatziakis",
"Donghan Yu",
"Kevin Jamieson",
"Simon Shaolei Du",
"Yelong Shen"
] |
2024-07-09T08:24:06Z
|
2024-07-02T10:09:19Z
|
2305.11567
|
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time
Series
|
Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations and the application of existing and new data-intensive ML methods. A possible solution to this bottleneck is to generate synthetic data. In this work, we introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series. TSGM includes a broad repertoire of machine learning methods: generative models, probabilistic, and simulator-based approaches. The framework enables users to evaluate the quality of the produced data from different angles: similarity, downstream effectiveness, predictive consistency, diversity, and privacy. The framework is extensible, which allows researchers to rapidly implement their own methods and compare them in a shareable environment. TSGM was tested on open datasets and in production and proved to be beneficial in both cases. Additionally to the library, the project allows users to employ command line interfaces for synthetic data generation which lowers the entry threshold for those without a programming background.
|
http://arxiv.org/pdf/2305.11567v2
|
[
"Alexander Nikitin",
"Letizia Iannucci",
"Samuel Kaski"
] |
2024-07-09T08:19:23Z
|
2023-05-19T10:11:21Z
|
2407.06646
|
Variational Learning ISTA
|
Compressed sensing combines the power of convex optimization techniques with a sparsity-inducing prior on the signal space to solve an underdetermined system of equations. For many problems, the sparsifying dictionary is not directly given, nor its existence can be assumed. Besides, the sensing matrix can change across different scenarios. Addressing these issues requires solving a sparse representation learning problem, namely dictionary learning, taking into account the epistemic uncertainty of the learned dictionaries and, finally, jointly learning sparse representations and reconstructions under varying sensing matrix conditions. We address both concerns by proposing a variant of the LISTA architecture. First, we introduce Augmented Dictionary Learning ISTA (A-DLISTA), which incorporates an augmentation module to adapt parameters to the current measurement setup. Then, we propose to learn a distribution over dictionaries via a variational approach, dubbed Variational Learning ISTA (VLISTA). VLISTA exploits A-DLISTA as the likelihood model and approximates a posterior distribution over the dictionaries as part of an unfolded LISTA-based recovery algorithm. As a result, VLISTA provides a probabilistic way to jointly learn the dictionary distribution and the reconstruction algorithm with varying sensing matrices. We provide theoretical and experimental support for our architecture and show that our model learns calibrated uncertainties.
|
http://arxiv.org/pdf/2407.06646v1
|
[
"Fabio Valerio Massoli",
"Christos Louizos",
"Arash Behboodi"
] |
2024-07-09T08:17:06Z
|
2024-07-09T08:17:06Z
|
2407.06637
|
Early Detection of Network Service Degradation: An Intra-Flow Approach
|
This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet Inter-Arrival Time (PIAT) values and other derived metrics, to infer the behavior of non-observable (NO) segments. Through a comprehensive evaluation, we identify an optimal O/NO split threshold of 10 observed delay samples, balancing prediction accuracy and resource utilization. Evaluating models including Logistic Regression, XGBoost, and Multi-Layer Perceptron, we find XGBoost outperforms others, achieving an F1-score of 0.74, balanced accuracy of 0.84, and AUROC of 0.97. Our findings highlight the effectiveness of incorporating comprehensive early flow features and the potential of our method to offer a practical solution for monitoring network traffic in resource-constrained environments. This approach ensures enhanced user experience and network performance by preemptively addressing potential SD, providing the basis for a robust framework for maintaining high-quality network services.
|
http://arxiv.org/pdf/2407.06637v1
|
[
"Balint Bicski",
"Adrian Pekar"
] |
2024-07-09T08:05:14Z
|
2024-07-09T08:05:14Z
|
2304.05655
|
Localisation of Regularised and Multiview Support Vector Machine
Learning
|
We prove a few representer theorems for a localised version of the regularised and multiview support vector machine learning problem introduced by H.Q. Minh, L. Bazzani, and V. Murino, Journal of Machine Learning Research, 17(2016) 1-72, that involves operator valued positive semidefinite kernels and their reproducing kernel Hilbert spaces. The results concern general cases when convex or nonconvex loss functions and finite or infinite dimensional input spaces are considered. We show that the general framework allows infinite dimensional input spaces and nonconvex loss functions for some special cases, in particular in case the loss functions are Gateaux differentiable. Detailed calculations are provided for the exponential least square loss function that lead to partially nonlinear equations for which a particular unconstrained potential reduction Newton's approximation method can be used.
|
http://arxiv.org/pdf/2304.05655v3
|
[
"Aurelian Gheondea",
"Cankat Tilki"
] |
2024-07-09T07:43:12Z
|
2023-04-12T07:19:02Z
|
2403.11780
|
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural
Language Prompt
|
Recent singing-voice-synthesis (SVS) methods have achieved remarkable audio quality and naturalness, yet they lack the capability to control the style attributes of the synthesized singing explicitly. We propose Prompt-Singer, the first SVS method that enables attribute controlling on singer gender, vocal range and volume with natural language. We adopt a model architecture based on a decoder-only transformer with a multi-scale hierarchy, and design a range-melody decoupled pitch representation that enables text-conditioned vocal range control while keeping melodic accuracy. Furthermore, we explore various experiment settings, including different types of text representations, text encoder fine-tuning, and introducing speech data to alleviate data scarcity, aiming to facilitate further research. Experiments show that our model achieves favorable controlling ability and audio quality. Audio samples are available at http://prompt-singer.github.io .
|
http://arxiv.org/pdf/2403.11780v2
|
[
"Yongqi Wang",
"Ruofan Hu",
"Rongjie Huang",
"Zhiqing Hong",
"Ruiqi Li",
"Wenrui Liu",
"Fuming You",
"Tao Jin",
"Zhou Zhao"
] |
2024-07-09T07:40:52Z
|
2024-03-18T13:39:05Z
|
2407.06612
|
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A
Review
|
Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate's complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue.
|
http://arxiv.org/pdf/2407.06612v1
|
[
"Rui Jin",
"Derun Li",
"Dehui Xiang",
"Lei Zhang",
"Hailing Zhou",
"Fei Shi",
"Weifang Zhu",
"Jing Cai",
"Tao Peng",
"Xinjian Chen"
] |
2024-07-09T07:36:18Z
|
2024-07-09T07:36:18Z
|
2407.06608
|
Iteratively Refined Image Reconstruction with Learned Attentive
Regularizers
|
We propose a regularization scheme for image reconstruction that leverages the power of deep learning while hinging on classic sparsity-promoting models. Many deep-learning-based models are hard to interpret and cumbersome to analyze theoretically. In contrast, our scheme is interpretable because it corresponds to the minimization of a series of convex problems. For each problem in the series, a mask is generated based on the previous solution to refine the regularization strength spatially. In this way, the model becomes progressively attentive to the image structure. For the underlying update operator, we prove the existence of a fixed point. As a special case, we investigate a mask generator for which the fixed-point iterations converge to a critical point of an explicit energy functional. In our experiments, we match the performance of state-of-the-art learned variational models for the solution of inverse problems. Additionally, we offer a promising balance between interpretability, theoretical guarantees, reliability, and performance.
|
http://arxiv.org/pdf/2407.06608v1
|
[
"Mehrsa Pourya",
"Sebastian Neumayer",
"Michael Unser"
] |
2024-07-09T07:22:48Z
|
2024-07-09T07:22:48Z
|
2407.03668
|
Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP
with Application of Beamforming Optimization
|
In this paper, we investigate a special class of quadratic-constrained quadratic programming (QCQP) with semi-definite constraints. Traditionally, since such a problem is non-convex and N-hard, the neural network (NN) is regarded as a promising method to obtain a high-performing solution. However, due to the inherent prediction error, it is challenging to ensure all solution output by the NN is feasible. Although some existing methods propose some naive methods, they only focus on reducing the constraint violation probability, where not all solutions are feasibly guaranteed. To deal with the above challenge, in this paper a computing efficient and reliable projection is proposed, where all solution output by the NN are ensured to be feasible. Moreover, unsupervised learning is used, so the NN can be trained effectively and efficiently without labels. Theoretically, the solution of the NN after projection is proven to be feasible, and we also prove the projection method can enhance the convergence performance and speed of the NN. To evaluate our proposed method, the quality of service (QoS)-contained beamforming scenario is studied, where the simulation results show the proposed method can achieve high-performance which is competitive with the lower bound.
|
http://arxiv.org/pdf/2407.03668v2
|
[
"Xiucheng Wang",
"Qi Qiu",
"Nan Cheng"
] |
2024-07-09T07:22:42Z
|
2024-07-04T06:26:01Z
|
2402.12072
|
Robustness and Exploration of Variational and Machine Learning
Approaches to Inverse Problems: An Overview
|
This paper provides an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data-consistent solutions through explicit guidance to satisfy specific semantic or textural properties.
|
http://arxiv.org/pdf/2402.12072v2
|
[
"Alexander Auras",
"Kanchana Vaishnavi Gandikota",
"Hannah Droege",
"Michael Moeller"
] |
2024-07-09T07:13:56Z
|
2024-02-19T11:48:11Z
|
2407.07924
|
Solving General Natural-Language-Description Optimization Problems with
Large Language Models
|
Optimization problems seek to find the best solution to an objective under a set of constraints, and have been widely investigated in real-world applications. Modeling and solving optimization problems in a specific domain typically require a combination of domain knowledge, mathematical skills, and programming ability, making it difficult for general users and even domain professionals. In this paper, we propose a novel framework called OptLLM that augments LLMs with external solvers. Specifically, OptLLM accepts user queries in natural language, convert them into mathematical formulations and programming codes, and calls the solvers to calculate the results for decision-making. In addition, OptLLM supports multi-round dialogues to gradually refine the modeling and solving of optimization problems. To illustrate the effectiveness of OptLLM, we provide tutorials on three typical optimization applications and conduct experiments on both prompt-based GPT models and a fine-tuned Qwen model using a large-scale selfdeveloped optimization dataset. Experimental results show that OptLLM works with various LLMs, and the fine-tuned model achieves an accuracy boost compared to the promptbased models. Some features of OptLLM framework have been available for trial since June 2023 (https://opt.alibabacloud.com/chat or https://opt.aliyun.com/chat).
|
http://arxiv.org/pdf/2407.07924v1
|
[
"Jihai Zhang",
"Wei Wang",
"Siyan Guo",
"Li Wang",
"Fangquan Lin",
"Cheng Yang",
"Wotao Yin"
] |
2024-07-09T07:11:10Z
|
2024-07-09T07:11:10Z
|
2404.07698
|
Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents
Probability Estimator
|
The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs.
|
http://arxiv.org/pdf/2404.07698v2
|
[
"Daniele Mari",
"André F. R. Guarda",
"Nuno M. M. Rodrigues",
"Simone Milani",
"Fernando Pereira"
] |
2024-07-09T06:56:06Z
|
2024-04-11T12:44:15Z
|
2304.00709
|
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected
Reconstruction
|
AutoEncoders (AEs) are commonly used for machine learning tasks due to their intrinsic learning ability. This unique characteristic can be capitalized for Outlier Detection (OD). However conventional AE-based methods face the issue of overconfident decisions and unexpected reconstruction results of outliers, limiting their performance in OD. To mitigate these issues, the Mean Squared Error (MSE) and Negative Logarithmic Likelihood (NLL) were firstly analyzed, and the importance of incorporating aleatoric uncertainty to AE-based OD was elucidated. Then the Weighted Negative Logarithmic Likelihood (WNLL) was proposed to adjust for the effect of uncertainty for different OD scenarios. Moreover, the Mean-Shift Scoring (MSS) method was proposed to utilize the local relationship of data to reduce the issue of false inliers caused by AE. Experiments on 32 real-world OD datasets proved the effectiveness of the proposed methods. The combination of WNLL and MSS achieved 41% relative performance improvement compared to the best baseline. In addition, MSS improved the detection performance of multiple AE-based outlier detectors by an average of 20%. The proposed methods have the potential to advance AE's development in OD.
|
http://arxiv.org/pdf/2304.00709v3
|
[
"Xu Tan",
"Jiawei Yang",
"Junqi Chen",
"Sylwan Rahardja",
"Susanto Rahardja"
] |
2024-07-09T06:08:17Z
|
2023-04-03T04:01:29Z
|
2405.12807
|
FAdam: Adam is a natural gradient optimizer using diagonal empirical
Fisher information
|
This paper establishes a mathematical foundation for the Adam optimizer, elucidating its connection to natural gradient descent through Riemannian and information geometry. We rigorously analyze the diagonal empirical Fisher information matrix (FIM) in Adam, clarifying all detailed approximations and advocating for the use of log probability functions as loss, which should be based on discrete distributions, due to the limitations of empirical FIM. Our analysis uncovers flaws in the original Adam algorithm, leading to proposed corrections such as enhanced momentum calculations, adjusted bias corrections, adaptive epsilon, and gradient clipping. We refine the weight decay term based on our theoretical framework. Our modified algorithm, Fisher Adam (FAdam), demonstrates superior performance across diverse domains including LLM, ASR, and VQ-VAE, achieving state-of-the-art results in ASR.
|
http://arxiv.org/pdf/2405.12807v8
|
[
"Dongseong Hwang"
] |
2024-07-09T05:15:47Z
|
2024-05-21T13:58:17Z
|
2407.06549
|
AutoTask: Task Aware Multi-Faceted Single Model for Multi-Task Ads
Relevance
|
Ads relevance models are crucial in determining the relevance between user search queries and ad offers, often framed as a classification problem. The complexity of modeling increases significantly with multiple ad types and varying scenarios that exhibit both similarities and differences. In this work, we introduce a novel multi-faceted attention model that performs task aware feature combination and cross task interaction modeling. Our technique formulates the feature combination problem as "language" modeling with auto-regressive attentions across both feature and task dimensions. Specifically, we introduce a new dimension of task ID encoding for task representations, thereby enabling precise relevance modeling across diverse ad scenarios with substantial improvement in generality capability for unseen tasks. We demonstrate that our model not only effectively handles the increased computational and maintenance demands as scenarios proliferate, but also outperforms generalized DNN models and even task-specific models across a spectrum of ad applications using a single unified model.
|
http://arxiv.org/pdf/2407.06549v1
|
[
"Shouchang Guo",
"Sonam Damani",
"Keng-hao Chang"
] |
2024-07-09T05:13:45Z
|
2024-07-09T05:13:45Z
|
2407.06544
|
Multiple Instance Verification
|
We explore multiple-instance verification, a problem setting where a query instance is verified against a bag of target instances with heterogeneous, unknown relevancy. We show that naive adaptations of attention-based multiple instance learning (MIL) methods and standard verification methods like Siamese neural networks are unsuitable for this setting: directly combining state-of-the-art (SOTA) MIL methods and Siamese networks is shown to be no better, and sometimes significantly worse, than a simple baseline model. Postulating that this may be caused by the failure of the representation of the target bag to incorporate the query instance, we introduce a new pooling approach named ``cross-attention pooling'' (CAP). Under the CAP framework, we propose two novel attention functions to address the challenge of distinguishing between highly similar instances in a target bag. Through empirical studies on three different verification tasks, we demonstrate that CAP outperforms adaptations of SOTA MIL methods and the baseline by substantial margins, in terms of both classification accuracy and quality of the explanations provided for the classifications. Ablation studies confirm the superior ability of the new attention functions to identify key instances.
|
http://arxiv.org/pdf/2407.06544v1
|
[
"Xin Xu",
"Eibe Frank",
"Geoffrey Holmes"
] |
2024-07-09T04:51:22Z
|
2024-07-09T04:51:22Z
|
2406.03230
|
Defending Large Language Models Against Attacks With Residual Stream
Activation Analysis
|
The widespread adoption of Large Language Models (LLMs), exemplified by OpenAI's ChatGPT, brings to the forefront the imperative to defend against adversarial threats on these models. These attacks, which manipulate an LLM's output by introducing malicious inputs, undermine the model's integrity and the trust users place in its outputs. In response to this challenge, our paper presents an innovative defensive strategy, given white box access to an LLM, that harnesses residual activation analysis between transformer layers of the LLM. We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification. We curate multiple datasets to demonstrate how this method of classification has high accuracy across multiple types of attack scenarios, including our newly-created attack dataset. Furthermore, we enhance the model's resilience by integrating safety fine-tuning techniques for LLMs in order to measure its effect on our capability to detect attacks. The results underscore the effectiveness of our approach in enhancing the detection and mitigation of adversarial inputs, advancing the security framework within which LLMs operate.
|
http://arxiv.org/pdf/2406.03230v3
|
[
"Amelia Kawasaki",
"Andrew Davis",
"Houssam Abbas"
] |
2024-07-09T04:39:46Z
|
2024-06-05T13:06:33Z
|
2407.06543
|
DriftGAN: Using historical data for Unsupervised Recurring Drift
Detection
|
In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift. Such concept drifts degrade the model's prediction performance, and therefore we require methods to overcome these issues. The initial step is to identify concept drifts and have a training method in place to recover the model's performance. Most concept drift detection methods work on detecting concept drifts and signalling the requirement to retrain the model. However, in real-world cases, there could be concept drifts that recur over a period of time. In this paper, we present an unsupervised method based on Generative Adversarial Networks(GAN) to detect concept drifts and identify whether a specific concept drift occurred in the past. Our method reduces the time and data the model requires to get up to speed for recurring drifts. Our key results indicate that our proposed model can outperform the current state-of-the-art models in most datasets. We also test our method on a real-world use case from astrophysics, where we detect the bow shock and magnetopause crossings with better results than the existing methods in the domain.
|
http://arxiv.org/abs/2407.06543v1
|
[
"Christofer Fellicious",
"Sahib Julka",
"Lorenz Wendlinger",
"Michael Granitzer"
] |
2024-07-09T04:38:44Z
|
2024-07-09T04:38:44Z
|
2407.06533
|
LETS-C: Leveraging Language Embedding for Time Series Classification
|
Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time series classification tasks has achieved state-of-the-art (SOTA) performance on standard benchmarks. However, these LLM-based models have a significant drawback due to the large model size, with the number of trainable parameters in the millions. In this paper, we propose an alternative approach to leveraging the success of language modeling in the time series domain. Instead of fine-tuning LLMs, we utilize a language embedding model to embed time series and then pair the embeddings with a simple classification head composed of convolutional neural networks (CNN) and multilayer perceptron (MLP). We conducted extensive experiments on well-established time series classification benchmark datasets. We demonstrated LETS-C not only outperforms the current SOTA in classification accuracy but also offers a lightweight solution, using only 14.5% of the trainable parameters on average compared to the SOTA model. Our findings suggest that leveraging language encoders to embed time series data, combined with a simple yet effective classification head, offers a promising direction for achieving high-performance time series classification while maintaining a lightweight model architecture.
|
http://arxiv.org/pdf/2407.06533v1
|
[
"Rachneet Kaur",
"Zhen Zeng",
"Tucker Balch",
"Manuela Veloso"
] |
2024-07-09T04:07:57Z
|
2024-07-09T04:07:57Z
|
2407.06529
|
Advanced Financial Fraud Detection Using GNN-CL Model
|
The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model's understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods.
|
http://arxiv.org/pdf/2407.06529v1
|
[
"Yu Cheng",
"Junjie Guo",
"Shiqing Long",
"You Wu",
"Mengfang Sun",
"Rong Zhang"
] |
2024-07-09T03:59:06Z
|
2024-07-09T03:59:06Z
|
2403.07362
|
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine
Unlearning
|
The trustworthy machine learning (ML) community is increasingly recognizing the crucial need for models capable of selectively 'unlearning' data points after training. This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning. Despite various MU methods for data influence erasure, evaluations have largely focused on random data forgetting, ignoring the vital inquiry into which subset should be chosen to truly gauge the authenticity of unlearning performance. To tackle this issue, we introduce a new evaluative angle for MU from an adversarial viewpoint. We propose identifying the data subset that presents the most significant challenge for influence erasure, i.e., pinpointing the worst-case forget set. Utilizing a bi-level optimization principle, we amplify unlearning challenges at the upper optimization level to emulate worst-case scenarios, while simultaneously engaging in standard training and unlearning at the lower level, achieving a balance between data influence erasure and model utility. Our proposal offers a worst-case evaluation of MU's resilience and effectiveness. Through extensive experiments across different datasets (including CIFAR-10, 100, CelebA, Tiny ImageNet, and ImageNet) and models (including both image classifiers and generative models), we expose critical pros and cons in existing (approximate) unlearning strategies. Our results illuminate the complex challenges of MU in practice, guiding the future development of more accurate and robust unlearning algorithms. The code is available at https://github.com/OPTML-Group/Unlearn-WorstCase.
|
http://arxiv.org/pdf/2403.07362v4
|
[
"Chongyu Fan",
"Jiancheng Liu",
"Alfred Hero",
"Sijia Liu"
] |
2024-07-09T03:59:01Z
|
2024-03-12T06:50:32Z
|
2309.10485
|
Exploring Sentence Type Effects on the Lombard Effect and
Intelligibility Enhancement: A Comparative Study of Natural and Grid
Sentences
|
This study explores how sentence types affect the Lombard effect and intelligibility enhancement, focusing on comparisons between natural and grid sentences. Using the Lombard Chinese-TIMIT (LCT) corpus and the Enhanced MAndarin Lombard Grid (EMALG) corpus, we analyze changes in phonetic and acoustic features across different noise levels. Our results show that grid sentences produce more pronounced Lombard effects than natural sentences. Then, we develop and test a normal-to-Lombard conversion model, trained separately on LCT and EMALG corpora. Through subjective and objective evaluations, natural sentences are superior in maintaining speech quality in intelligibility enhancement. In contrast, grid sentences could provide superior intelligibility due to the more pronounced Lombard effect. This study provides a valuable perspective on enhancing speech communication in noisy environments.
|
http://arxiv.org/pdf/2309.10485v2
|
[
"Hongyang Chen",
"Yuhong Yang",
"Zhongyuan Wang",
"Weiping Tu",
"Haojun Ai",
"Song Lin"
] |
2024-07-09T03:32:54Z
|
2023-09-19T09:54:36Z
|
2407.06518
|
Graph Neural Networks and Deep Reinforcement Learning Based Resource
Allocation for V2X Communications
|
In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.
|
http://arxiv.org/pdf/2407.06518v1
|
[
"Maoxin Ji",
"Qiong Wu",
"Pingyi Fan",
"Nan Cheng",
"Wen Chen",
"Jiangzhou Wang",
"Khaled B. Letaief"
] |
2024-07-09T03:14:11Z
|
2024-07-09T03:14:11Z
|
2405.20358
|
Medication Recommendation via Dual Molecular Modalities and
Multi-Substructure Enhancement
|
Medication recommendation combines patient medical history with biomedical knowledge to assist doctors in determining medication combinations more accurately and safely. Existing works based on molecular knowledge neglect the 3D geometric structure of molecules and fail to learn the high-dimensional information of medications, leading to structural confusion. Additionally, it does not extract key substructures from a single patient visit, resulting in the failure to identify medication molecules suitable for the current patient visit. To address the above limitations, we propose a bimodal molecular recommendation framework named BiMoRec, which introduces 3D molecular structures to obtain atomic 3D coordinates and edge indices, overcoming the inherent lack of high-dimensional molecular information in 2D molecular structures. To retain the fast training and prediction efficiency of the recommendation system, we use bimodal graph contrastive pretraining to maximize the mutual information between the two molecular modalities, achieving the fusion of 2D and 3D molecular graphs and re-evaluating substructures at the visit level. Specifically, we use deep learning networks to construct a pretraining method that acquires 2D and 3D molecular structure representations and substructure representations, and obtain mutual information through contrastive learning. We then generate fused molecular representations using the trained GNN module and re-determine the relevance of substructure representations in combination with the patient's clinical history. Finally, we generate the final medication combination based on the extracted substructure sequences. Our implementation on the MIMIC-III and MIMIC-IV datasets demonstrates that our method achieves state-of-the-art performance. Compared to the second-best baseline, our model improves accuracy by 2.07%, with DDI at the same level as the baseline.
|
http://arxiv.org/pdf/2405.20358v2
|
[
"Shi Mu",
"Shunpan Liang",
"Xiang Li"
] |
2024-07-09T03:13:12Z
|
2024-05-30T07:13:08Z
|
2407.04370
|
Regulating Model Reliance on Non-Robust Features by Smoothing Input
Marginal Density
|
Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach, robust feature attributions exhibit a certain consistency, while non-robust feature attributions are susceptible to fluctuations. This behavior allows identification of correlation between model reliance on non-robust features and smoothness of marginal density of the input samples. Hence, we uniquely regularize the gradients of the marginal density w.r.t. the input features for robustness. We also devise an efficient implementation of our regularization to address the potential numerical instability of the underlying optimization process. Moreover, we analytically reveal that, as opposed to our marginal density smoothing, the prevalent input gradient regularization smoothens conditional or joint density of the input, which can cause limited robustness. Our experiments validate the effectiveness of the proposed method, providing clear evidence of its capability to address the feature leakage problem and mitigate spurious correlations. Extensive results further establish that our technique enables the model to exhibit robustness against perturbations in pixel values, input gradients, and density.
|
http://arxiv.org/pdf/2407.04370v2
|
[
"Peiyu Yang",
"Naveed Akhtar",
"Mubarak Shah",
"Ajmal Mian"
] |
2024-07-09T03:09:41Z
|
2024-07-05T09:16:56Z
|
2407.07124
|
FedClust: Tackling Data Heterogeneity in Federated Learning through
Weight-Driven Client Clustering
|
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without exposing their local data. One of the major challenges in FL is the presence of uneven data distributions across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. To address the performance degradation issue incurred by such data heterogeneity, clustered federated learning (CFL) shows its promise by grouping clients into separate learning clusters based on the similarity of their local data distributions. However, state-of-the-art CFL approaches require a large number of communication rounds to learn the distribution similarities during training until the formation of clusters is stabilized. Moreover, some of these algorithms heavily rely on a predefined number of clusters, thus limiting their flexibility and adaptability. In this paper, we propose {em FedClust}, a novel approach for CFL that leverages the correlation between local model weights and the data distribution of clients. {em FedClust} groups clients into clusters in a one-shot manner by measuring the similarity degrees among clients based on the strategically selected partial weights of locally trained models. We conduct extensive experiments on four benchmark datasets with different non-IID data settings. Experimental results demonstrate that {em FedClust} achieves higher model accuracy up to $sim$45% as well as faster convergence with a significantly reduced communication cost up to 2.7$times$ compared to its state-of-the-art counterparts.
|
http://arxiv.org/pdf/2407.07124v1
|
[
"Md Sirajul Islam",
"Simin Javaherian",
"Fei Xu",
"Xu Yuan",
"Li Chen",
"Nian-Feng Tzeng"
] |
2024-07-09T02:47:16Z
|
2024-07-09T02:47:16Z
|
2407.06507
|
Economic span selection of bridge based on deep reinforcement learning
|
Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoretically analyzed, and the theoretical solution formula of economic span is deduced. Construction process of bridge simulation environment is described in detail, including observation space, action space and reward function of the environment. Agent is constructed, convolutional neural network is used to approximate Q function,{epsilon} greedy policy is used for action selection, and experience replay is used for training. The test verifies that the agent can successfully learn optimal policy and realize economic span selection of bridge. This study provides a potential decision-making tool for bridge design.
|
http://arxiv.org/pdf/2407.06507v1
|
[
"Leye Zhang",
"Xiangxiang Tian",
"Chengli Zhang",
"Hongjun Zhang"
] |
2024-07-09T02:27:52Z
|
2024-07-09T02:27:52Z
|
2402.14982
|
Human Brain Exhibits Distinct Patterns When Listening to Fake Versus
Real Audio: Preliminary Evidence
|
In this paper we study the variations in human brain activity when listening to real and fake audio. Our preliminary results suggest that the representations learned by a state-of-the-art deepfake audio detection algorithm, do not exhibit clear distinct patterns between real and fake audio. In contrast, human brain activity, as measured by EEG, displays distinct patterns when individuals are exposed to fake versus real audio. This preliminary evidence enables future research directions in areas such as deepfake audio detection.
|
http://arxiv.org/pdf/2402.14982v3
|
[
"Mahsa Salehi",
"Kalin Stefanov",
"Ehsan Shareghi"
] |
2024-07-09T02:21:21Z
|
2024-02-22T21:44:58Z
|
2310.15690
|
Power-Enhanced Residual Network for Function Approximation and
Physics-Informed Inverse Problems
|
In this study, we investigate how the updating of weights during forward operation and the computation of gradients during backpropagation impact the optimization process, training procedure, and overall performance of the neural network, particularly the multi-layer perceptrons (MLPs). This paper introduces a novel neural network structure called the Power-Enhancing residual network, inspired by highway network and residual network, designed to improve the network's capabilities for both smooth and non-smooth functions approximation in 2D and 3D settings. By incorporating power terms into residual elements, the architecture enhances the stability of weight updating, thereby facilitating better convergence and accuracy. The study explores network depth, width, and optimization methods, showing the architecture's adaptability and performance advantages. Consistently, the results emphasize the exceptional accuracy of the proposed Power-Enhancing residual network, particularly for non-smooth functions. Real-world examples also confirm its superiority over plain neural network in terms of accuracy, convergence, and efficiency. Moreover, the proposed architecture is also applied to solving the inverse Burgers' equation, demonstrating superior performance. In conclusion, the Power-Enhancing residual network offers a versatile solution that significantly enhances neural network capabilities by emphasizing the importance of stable weight updates for effective training in deep neural networks. The codes implemented are available at: url{https://github.com/CMMAi/ResNet_for_PINN}.
|
http://arxiv.org/abs/2310.15690v2
|
[
"Amir Noorizadegan",
"D. L. Young",
"Y. C. Hon",
"C. S. Chen"
] |
2024-07-09T02:19:26Z
|
2023-10-24T10:01:15Z
|
2407.02431
|
On the Robustness of Graph Reduction Against GNN Backdoor
|
Graph Neural Networks (GNNs) are gaining popularity across various domains due to their effectiveness in learning graph-structured data. Nevertheless, they have been shown to be susceptible to backdoor poisoning attacks, which pose serious threats to real-world applications. Meanwhile, graph reduction techniques, including coarsening and sparsification, which have long been employed to improve the scalability of large graph computational tasks, have recently emerged as effective methods for accelerating GNN training on large-scale graphs. However, the current development and deployment of graph reduction techniques for large graphs overlook the potential risks of data poisoning attacks against GNNs. It is not yet clear how graph reduction interacts with existing backdoor attacks. This paper conducts a thorough examination of the robustness of graph reduction methods in scalable GNN training in the presence of state-of-the-art backdoor attacks. We performed a comprehensive robustness analysis across six coarsening methods and six sparsification methods for graph reduction, under three GNN backdoor attacks against three GNN architectures. Our findings indicate that the effectiveness of graph reduction methods in mitigating attack success rates varies significantly, with some methods even exacerbating the attacks. Through detailed analyses of triggers and poisoned nodes, we interpret our findings and enhance our understanding of how graph reduction influences robustness against backdoor attacks. These results highlight the critical need for incorporating robustness considerations in graph reduction for GNN training, ensuring that enhancements in computational efficiency do not compromise the security of GNN systems.
|
http://arxiv.org/pdf/2407.02431v2
|
[
"Yuxuan Zhu",
"Michael Mandulak",
"Kerui Wu",
"George Slota",
"Yuseok Jeon",
"Ka-Ho Chow",
"Lei Yu"
] |
2024-07-09T02:11:47Z
|
2024-07-02T17:08:38Z
|
2407.06503
|
Preference-Guided Reinforcement Learning for Efficient Exploration
|
In this paper, we investigate preference-based reinforcement learning (PbRL) that allows reinforcement learning (RL) agents to learn from human feedback. This is particularly valuable when defining a fine-grain reward function is not feasible. However, this approach is inefficient and impractical for promoting deep exploration in hard-exploration tasks with long horizons and sparse rewards. To tackle this issue, we introduce LOPE: Learning Online with trajectory Preference guidancE, an end-to-end preference-guided RL framework that enhances exploration efficiency in hard-exploration tasks. Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance, avoiding learning a separate reward model from preferences. Specifically, LOPE includes a two-step sequential policy optimization process consisting of trust-region-based policy improvement and preference guidance steps. We reformulate preference guidance as a novel trajectory-wise state marginal matching problem that minimizes the maximum mean discrepancy distance between the preferred trajectories and the learned policy. Furthermore, we provide a theoretical analysis to characterize the performance improvement bound and evaluate the LOPE's effectiveness. When assessed in various challenging hard-exploration environments, LOPE outperforms several state-of-the-art methods regarding convergence rate and overall performance. The code used in this study is available at url{https://github.com/buaawgj/LOPE}.
|
http://arxiv.org/pdf/2407.06503v1
|
[
"Guojian Wang",
"Faguo Wu",
"Xiao Zhang",
"Tianyuan Chen",
"Xuyang Chen",
"Lin Zhao"
] |
2024-07-09T02:11:12Z
|
2024-07-09T02:11:12Z
|
2407.06496
|
It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With
Non-Convex Loss
|
Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular iterative algorithm used to train machine learning models while formally guaranteeing the privacy of users. However the privacy analysis of DP-SGD makes the unrealistic assumption that all intermediate iterates (aka internal state) of the algorithm are released since in practice, only the final trained model, i.e., the final iterate of the algorithm is released. In this hidden state setting, prior work has provided tighter analyses, albeit only when the loss function is constrained, e.g., strongly convex and smooth or linear. On the other hand, the privacy leakage observed empirically from hidden state DP-SGD, even when using non-convex loss functions suggest that there is in fact a gap between the theoretical privacy analysis and the privacy guarantees achieved in practice. Therefore, it remains an open question whether privacy amplification for DP-SGD is possible in the hidden state setting for general loss functions. Unfortunately, this work answers the aforementioned research question negatively. By carefully constructing a loss function for DP-SGD, we show that for specific loss functions, the final iterate of DP-SGD alone leaks as much information as the sequence of all iterates combined. Furthermore, we empirically verify this result by evaluating the privacy leakage from the final iterate of DP-SGD with our loss function and show that this matches the theoretical upper bound guaranteed by DP exactly. Therefore, we show that the current privacy analysis fo DP-SGD is tight for general loss functions and conclude that no privacy amplification is possible for DP-SGD in general for all (possibly non-convex) loss functions.
|
http://arxiv.org/pdf/2407.06496v1
|
[
"Meenatchi Sundaram Muthu Selva Annamalai"
] |
2024-07-09T01:58:19Z
|
2024-07-09T01:58:19Z
|
2407.06494
|
A Generative Approach to Control Complex Physical Systems
|
Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computational costs. On the other hand, recent deep learning and reinforcement learning-based approaches often struggle to optimize long-term control sequences under the constraints of system dynamics. In this work, we introduce Diffusion Physical systems Control (DiffPhyCon), a new class of method to address the physical systems control problem. DiffPhyCon excels by simultaneously minimizing both the learned generative energy function and the predefined control objectives across the entire trajectory and control sequence. Thus, it can explore globally and identify near-optimal control sequences. Moreover, we enhance DiffPhyCon with prior reweighting, enabling the discovery of control sequences that significantly deviate from the training distribution. We test our method in 1D Burgers' equation and 2D jellyfish movement control in a fluid environment. Our method outperforms widely applied classical approaches and state-of-the-art deep learning and reinforcement learning methods. Notably, DiffPhyCon unveils an intriguing fast-close-slow-open pattern observed in the jellyfish, aligning with established findings in the field of fluid dynamics.
|
http://arxiv.org/pdf/2407.06494v1
|
[
"Long Wei",
"Peiyan Hu",
"Ruiqi Feng",
"Haodong Feng",
"Yixuan Du",
"Tao Zhang",
"Rui Wang",
"Yue Wang",
"Zhi-Ming Ma",
"Tailin Wu"
] |
2024-07-09T01:56:23Z
|
2024-07-09T01:56:23Z
|
2311.17352
|
Efficient Stitchable Task Adaptation
|
The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However, most fine-tuning methods are designed to meet a specific resource budget. Recently, considering diverse deployment scenarios with various resource budgets, SN-Net is introduced to quickly obtain numerous new networks (stitches) from the pre-trained models (anchors) in a model family via model stitching. Although promising, SN-Net confronts new challenges when adapting it to new target domains, including huge memory and storage requirements and a long and sub-optimal multistage adaptation process. In this work, we present a novel framework, Efficient Stitchable Task Adaptation (ESTA), to efficiently produce a palette of fine-tuned models that adhere to diverse resource constraints. Specifically, we first tailor parameter-efficient fine-tuning to share low-rank updates among the stitches while maintaining independent bias terms. In this way, we largely reduce fine-tuning memory burdens and mitigate the interference among stitches that arises in task adaptation. Furthermore, we streamline a simple yet effective one-stage deployment pipeline, which estimates the important stitches to deploy with training-time gradient statistics. By assigning higher sampling probabilities to important stitches, we also get a boosted Pareto frontier. Extensive experiments on 25 downstream visual recognition tasks demonstrate that our ESTA is capable of generating stitches with smooth accuracy-efficiency trade-offs and surpasses the direct SN-Net adaptation by remarkable margins with significantly lower training time and fewer trainable parameters. Furthermore, we demonstrate the flexibility and scalability of our ESTA framework by stitching LLMs from LLaMA family, obtaining chatbot stitches of assorted sizes. Source code is available at https://github.com/ziplab/Stitched_LLaMA
|
http://arxiv.org/pdf/2311.17352v2
|
[
"Haoyu He",
"Zizheng Pan",
"Jing Liu",
"Jianfei Cai",
"Bohan Zhuang"
] |
2024-07-09T01:54:18Z
|
2023-11-29T04:31:35Z
|
2312.01753
|
Long-Tail Learning with Rebalanced Contrastive Loss
|
Integrating supervised contrastive loss to cross entropy-based communication has recently been proposed as a solution to address the long-tail learning problem. However, when the class imbalance ratio is high, it requires adjusting the supervised contrastive loss to support the tail classes, as the conventional contrastive learning is biased towards head classes by default. To this end, we present Rebalanced Contrastive Learning (RCL), an efficient means to increase the long tail classification accuracy by addressing three main aspects: 1. Feature space balancedness - Equal division of the feature space among all the classes, 2. Intra-Class compactness - Reducing the distance between same-class embeddings, 3. Regularization - Enforcing larger margins for tail classes to reduce overfitting. RCL adopts class frequency-based SoftMax loss balancing to supervised contrastive learning loss and exploits scalar multiplied features fed to the contrastive learning loss to enforce compactness. We implement RCL on the Balanced Contrastive Learning (BCL) Framework, which has the SOTA performance. Our experiments on three benchmark datasets demonstrate the richness of the learnt embeddings and increased top-1 balanced accuracy RCL provides to the BCL framework. We further demonstrate that the performance of RCL as a standalone loss also achieves state-of-the-art level accuracy.
|
http://arxiv.org/pdf/2312.01753v2
|
[
"Charika De Alvis",
"Dishanika Denipitiyage",
"Suranga Seneviratne"
] |
2024-07-09T01:30:04Z
|
2023-12-04T09:27:03Z
|
2212.03560
|
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially
Observed Time Series
|
Ordinary Differential Equations (ODE) based models have become popular as foundation models for solving many time series problems. Combining neural ODEs with traditional RNN models has provided the best representation for irregular time series. However, ODE-based models typically require the trajectory of hidden states to be defined based on either the initial observed value or the most recent observation, raising questions about their effectiveness when dealing with longer sequences and extended time intervals. In this article, we explore the behaviour of the ODE models in the context of time series data with varying degrees of sparsity. We introduce SeqLink, an innovative neural architecture designed to enhance the robustness of sequence representation. Unlike traditional approaches that solely rely on the hidden state generated from the last observed value, SeqLink leverages ODE latent representations derived from multiple data samples, enabling it to generate robust data representations regardless of sequence length or data sparsity level. The core concept behind our model is the definition of hidden states for the unobserved values based on the relationships between samples (links between sequences). Through extensive experiments on partially observed synthetic and real-world datasets, we demonstrate that SeqLink improves the modelling of intermittent time series, consistently outperforming state-of-the-art approaches.
|
http://arxiv.org/pdf/2212.03560v2
|
[
"Futoon M. Abushaqra",
"Hao Xue",
"Yongli Ren",
"Flora D. Salim"
] |
2024-07-09T01:29:36Z
|
2022-12-07T10:25:59Z
|
2407.06488
|
Towards Understanding Multi-Task Learning (Generalization) of LLMs via
Detecting and Exploring Task-Specific Neurons
|
While large language models (LLMs) have demonstrated superior multi-task capabilities, understanding the learning mechanisms behind this is still a challenging problem. In this paper, we attempt to understand such mechanisms from the perspective of neurons. Specifically, we detect task-sensitive neurons in LLMs via gradient attribution on task-specific data. Through extensive deactivation and fine-tuning experiments, we demonstrate that the detected neurons are highly correlated with the given task, which we term as task-specific neurons. With these identified task-specific neurons, we delve into two common problems in multi-task learning and continuous learning: Generalization and Catastrophic Forgetting. We find that the overlap of task-specific neurons is strongly associated with generalization and specialization across tasks. Interestingly, at certain layers of LLMs, there is a high similarity in the parameters of different task-specific neurons, and such similarity is highly correlated with the generalization performance. Inspired by these findings, we propose a neuron-level continuous fine-tuning method that only fine-tunes the current task-specific neurons during continuous learning, and extensive experiments demonstrate the effectiveness of the proposed method. Our study provides insights into the interpretability of LLMs in multi-task learning.
|
http://arxiv.org/pdf/2407.06488v1
|
[
"Yongqi Leng",
"Deyi Xiong"
] |
2024-07-09T01:27:35Z
|
2024-07-09T01:27:35Z
|
2306.06559
|
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous
Updates
|
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular parameter server framework. In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset. However, the synchronization phase is sensitive to stragglers. An efficient way to mitigate this effect is to consider asynchronous updates, where each worker computes stochastic gradients and communicates with other workers at its own pace. Unfortunately, fully asynchronous updates suffer from staleness of stragglers' parameters. To address these limitations, we propose a fully decentralized algorithm DSGD-AAU with adaptive asynchronous updates via adaptively determining the number of neighbor workers for each worker to communicate with. We show that DSGD-AAU achieves a linear speedup for convergence and demonstrate its effectiveness via extensive experiments.
|
http://arxiv.org/pdf/2306.06559v2
|
[
"Guojun Xiong",
"Gang Yan",
"Shiqiang Wang",
"Jian Li"
] |
2024-07-09T01:23:59Z
|
2023-06-11T02:08:59Z
|
2407.06485
|
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
|
Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of Internet of Things (IoT) and Artificial Intelligence (AI) technologies. Although advanced deep learning techniques enhance the efficient data processing and intelligent analysis of complex IoT data, they still suffer from notable challenges when deployed to practical AIoT applications, such as constrained resources, and diverse task requirements. Knowledge transfer is an effective method to enhance learning performance by avoiding the exorbitant costs associated with data recollection and model retraining. Notably, although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances of various knowledge transfer techniques for AIoT field. This survey endeavors to introduce a new concept of knowledge transfer, referred to as Crowd Knowledge Transfer (CrowdTransfer), which aims to transfer prior knowledge learned from a crowd of agents to reduce the training cost and as well as improve the performance of the model in real-world complicated scenarios. Particularly, we present four transfer modes from the perspective of crowd intelligence, including derivation, sharing, evolution and fusion modes. Building upon conventional transfer learning methods, we further delve into advanced crowd knowledge transfer models from three perspectives for various AIoT applications. Furthermore, we explore some applications of AIoT areas, such as human activity recognition, urban computing, multi-robot system, and smart factory. Finally, we discuss the open issues and outline future research directions of knowledge transfer in AIoT community.
|
http://arxiv.org/abs/2407.06485v1
|
[
"Yan Liu",
"Bin Guo",
"Nuo Li",
"Yasan Ding",
"Zhouyangzi Zhang",
"Zhiwen Yu"
] |
2024-07-09T01:20:37Z
|
2024-07-09T01:20:37Z
|
2407.05494
|
Prospective Messaging: Learning in Networks with Communication Delays
|
Inter-neuron communication delays are ubiquitous in physically realized neural networks such as biological neural circuits and neuromorphic hardware. These delays have significant and often disruptive consequences on network dynamics during training and inference. It is therefore essential that communication delays be accounted for, both in computational models of biological neural networks and in large-scale neuromorphic systems. Nonetheless, communication delays have yet to be comprehensively addressed in either domain. In this paper, we first show that delays prevent state-of-the-art continuous-time neural networks called Latent Equilibrium (LE) networks from learning even simple tasks despite significant overparameterization. We then propose to compensate for communication delays by predicting future signals based on currently available ones. This conceptually straightforward approach, which we call prospective messaging (PM), uses only neuron-local information, and is flexible in terms of memory and computation requirements. We demonstrate that incorporating PM into delayed LE networks prevents reaction lags, and facilitates successful learning on Fourier synthesis and autoregressive video prediction tasks.
|
http://arxiv.org/pdf/2407.05494v2
|
[
"Ryan Fayyazi",
"Christian Weilbach",
"Frank Wood"
] |
2024-07-09T01:20:32Z
|
2024-07-07T20:54:14Z
|
2407.06483
|
Composable Interventions for Language Models
|
Test-time interventions for language models can enhance factual accuracy, mitigate harmful outputs, and improve model efficiency without costly retraining. But despite a flood of new methods, different types of interventions are largely developing independently. In practice, multiple interventions must be applied sequentially to the same model, yet we lack standardized ways to study how interventions interact. We fill this gap by introducing composable interventions, a framework to study the effects of using multiple interventions on the same language models, featuring new metrics and a unified codebase. Using our framework, we conduct extensive experiments and compose popular methods from three emerging intervention categories -- Knowledge Editing, Model Compression, and Machine Unlearning. Our results from 310 different compositions uncover meaningful interactions: compression hinders editing and unlearning, composing interventions hinges on their order of application, and popular general-purpose metrics are inadequate for assessing composability. Taken together, our findings showcase clear gaps in composability, suggesting a need for new multi-objective interventions. All of our code is public: https://github.com/hartvigsen-group/composable-interventions.
|
http://arxiv.org/pdf/2407.06483v1
|
[
"Arinbjorn Kolbeinsson",
"Kyle O'Brien",
"Tianjin Huang",
"Shanghua Gao",
"Shiwei Liu",
"Jonathan Richard Schwarz",
"Anurag Vaidya",
"Faisal Mahmood",
"Marinka Zitnik",
"Tianlong Chen",
"Thomas Hartvigsen"
] |
2024-07-09T01:17:44Z
|
2024-07-09T01:17:44Z
|
2403.09303
|
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical
Perspective
|
Medical anomaly detection aims to identify abnormal findings using only normal training data, playing a crucial role in health screening and recognizing rare diseases. Reconstruction-based methods, particularly those utilizing autoencoders (AEs), are dominant in this field. They work under the assumption that AEs trained on only normal data cannot reconstruct unseen abnormal regions well, thereby enabling the anomaly detection based on reconstruction errors. However, this assumption does not always hold due to the mismatch between the reconstruction training objective and the anomaly detection task objective, rendering these methods theoretically unsound. This study focuses on providing a theoretical foundation for AE-based reconstruction methods in anomaly detection. By leveraging information theory, we elucidate the principles of these methods and reveal that the key to improving AE in anomaly detection lies in minimizing the information entropy of latent vectors. Experiments on four datasets with two image modalities validate the effectiveness of our theory. To the best of our knowledge, this is the first effort to theoretically clarify the principles and design philosophy of AE for anomaly detection. The code is available at url{https://github.com/caiyu6666/AE4AD}.
|
http://arxiv.org/pdf/2403.09303v3
|
[
"Yu Cai",
"Hao Chen",
"Kwang-Ting Cheng"
] |
2024-07-09T01:14:41Z
|
2024-03-14T11:51:01Z
|
2404.08860
|
Enhancing Mobile "How-to" Queries with Automated Search Results
Verification and Reranking
|
Many people use search engines to find online guidance to solve computer or mobile device problems. Users frequently encounter challenges in identifying effective solutions from search results, often wasting time trying ineffective solutions that seem relevant yet fail to solve real problems. This paper introduces a novel approach to improving the accuracy and relevance of online technical support search results through automated search results verification and reranking. Taking "How-to" queries specific to on-device execution as a starting point, we developed the first solution that allows an AI agent to interpret and execute step-by-step instructions in the search results in a controlled Android environment. We further integrated the agent's findings into a reranking mechanism that orders search results based on the success indicators of the tested solutions. The paper details the architecture of our solution and a comprehensive evaluation of the system through a series of tests across various application domains. The results demonstrate a significant improvement in the quality and reliability of the top-ranked results. Our findings suggest a paradigm shift in how search engine ranking for online technical support help can be optimized, offering a scalable and automated solution to the pervasive challenge of finding effective and reliable online help.
|
http://arxiv.org/pdf/2404.08860v3
|
[
"Lei Ding",
"Jeshwanth Bheemanpally",
"Yi Zhang"
] |
2024-07-09T01:14:21Z
|
2024-04-13T00:20:09Z
|
2407.06481
|
Sinkhorn algorithms and linear programming solvers for optimal partial
transport problems
|
In this note, we generalize the classical optimal partial transport (OPT) problem by modifying the mass destruction/creation term to function-based terms, introducing what we term ``generalized optimal partial transport'' problems. We then discuss the dual formulation of these problems and the associated Sinkhorn solver. Finally, we explore how these new OPT problems relate to classical optimal transport (OT) problems and introduce a linear programming solver tailored for these generalized scenarios.
|
http://arxiv.org/pdf/2407.06481v1
|
[
"Yikun Bai"
] |
2024-07-09T01:08:21Z
|
2024-07-09T01:08:21Z
|
2001.03798
|
Bayesian Semi-supervised learning under nonparanormality
|
Semi-supervised learning is a model training method that uses both labeled and unlabeled data. This paper proposes a fully Bayes semi-supervised learning algorithm that can be applied to any binary classification problem. We assume the labels are missing at random when using unlabeled data in a semi-supervised setting. We assume that the observations follow two multivariate normal distributions depending on their true class labels after some common unknown transformation is applied to each component of the observation vector. The function is expanded in a B-splines series and a prior is put on the coefficients. We consider a normal prior on the coefficients and constrain the values to meet the requirement for normality and identifiability constraints. The precision matrices of the two Gaussian distributions have a conjugate Wishart prior, while the means have improper uniform priors. The resulting posterior is still conditionally conjugate, and the Gibbs sampler aided by a data augmentation technique can thus be adopted. An extensive simulation study compares the proposed method with several other available methods. The proposed method is also applied to real datasets on diagnosing breast cancer and classification of signals. We conclude that the proposed method has a better prediction accuracy in various cases.
|
http://arxiv.org/pdf/2001.03798v2
|
[
"Rui Zhu",
"Shuvrarghya Ghosh",
"Subhashis Ghosal"
] |
2024-07-09T01:03:09Z
|
2020-01-11T21:31:25Z
|
2310.13888
|
Towards a General Framework for Continual Learning with Pre-training
|
In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artificial intelligence systems to accommodate real-world dynamics. From a theoretical perspective, we decompose its objective into three hierarchical components, including within-task prediction, task-identity inference, and task-adaptive prediction. Then we propose an innovative approach to explicitly optimize these components with parameter-efficient fine-tuning (PEFT) techniques and representation statistics. We empirically demonstrate the superiority and generality of our approach in downstream continual learning, and further explore the applicability of PEFT techniques in upstream continual learning. We also discuss the biological basis of the proposed framework with recent advances in neuroscience.
|
http://arxiv.org/pdf/2310.13888v2
|
[
"Liyuan Wang",
"Jingyi Xie",
"Xingxing Zhang",
"Hang Su",
"Jun Zhu"
] |
2024-07-09T00:56:12Z
|
2023-10-21T02:03:38Z
|
2407.06459
|
How Much Progress Did I Make? An Unexplored Human Feedback Signal for
Teaching Robots
|
Enhancing the expressiveness of human teaching is vital for both improving robots' learning from humans and the human-teaching-robot experience. In this work, we characterize and test a little-used teaching signal: textit{progress}, designed to represent the completion percentage of a task. We conducted two online studies with 76 crowd-sourced participants and one public space study with 40 non-expert participants to validate the capability of this progress signal. We find that progress indicates whether the task is successfully performed, reflects the degree of task completion, identifies unproductive but harmless behaviors, and is likely to be more consistent across participants. Furthermore, our results show that giving progress does not require extra workload and time. An additional contribution of our work is a dataset of 40 non-expert demonstrations from the public space study through an ice cream topping-adding task, which we observe to be multi-policy and sub-optimal, with sub-optimality not only from teleoperation errors but also from exploratory actions and attempts. The dataset is available at url{https://github.com/TeachingwithProgress/Non-Expert_Demonstrations}.
|
http://arxiv.org/pdf/2407.06459v1
|
[
"Hang Yu",
"Qidi Fang",
"Shijie Fang",
"Reuben M. Aronson",
"Elaine Schaertl Short"
] |
2024-07-08T23:47:13Z
|
2024-07-08T23:47:13Z
|
2407.06447
|
Geospatial Trajectory Generation via Efficient Abduction: Deployment for
Independent Testing
|
The ability to generate artificial human movement patterns while meeting location and time constraints is an important problem in the security community, particularly as it enables the study of the analog problem of detecting such patterns while maintaining privacy. We frame this problem as an instance of abduction guided by a novel parsimony function represented as an aggregate truth value over an annotated logic program. This approach has the added benefit of affording explainability to an analyst user. By showing that any subset of such a program can provide a lower bound on this parsimony requirement, we are able to abduce movement trajectories efficiently through an informed (i.e., A*) search. We describe how our implementation was enhanced with the application of multiple techniques in order to be scaled and integrated with a cloud-based software stack that included bottom-up rule learning, geolocated knowledge graph retrieval/management, and interfaces with government systems for independently conducted government-run tests for which we provide results. We also report on our own experiments showing that we not only provide exact results but also scale to very large scenarios and provide realistic agent trajectories that can go undetected by machine learning anomaly detectors.
|
http://arxiv.org/pdf/2407.06447v1
|
[
"Divyagna Bavikadi",
"Dyuman Aditya",
"Devendra Parkar",
"Paulo Shakarian",
"Graham Mueller",
"Chad Parvis",
"Gerardo I. Simari"
] |
2024-07-08T23:11:47Z
|
2024-07-08T23:11:47Z
|
2401.12800
|
Deep Learning in Physical Layer: Review on Data Driven End-to-End
Communication Systems and their Enabling Semantic Applications
|
Deep learning (DL) has revolutionized wireless communication systems by introducing datadriven end-to-end (E2E) learning, where the physical layer (PHY) is transformed into DL architectures to achieve peak optimization. Leveraging DL for E2E optimization in PHY significantly enhances its adaptability and performance in complex wireless environments, meeting the demands of advanced network systems such as 5G and beyond. Furthermore, this evolution of data-driven PHY optimization has also enabled advanced semantic applications across various modalities, including text, image, audio, video, and multimodal transmissions. These applications elevate communication from bit-level to semantic-level intelligence, making it capable of discerning context and intent. Although the PHY, as a DL architecture, plays a crucial role in enabling semantic communication (SemCom) systems, comprehensive studies that integrate both E2E communication and SemCom systems remain significantly underexplored. This highlights the novelty and potential of these integrative fields, marking them as a promising research domain. Therefore, this article provides a comprehensive review of the emerging field of data-driven PHY for E2E communication systems, emphasizing their role in enabling semantic applications across various modalities. It also identifies key challenges and potential research directions, serving as a crucial guide for future advancements in DL for E2E communication and SemCom systems.
|
http://arxiv.org/abs/2401.12800v2
|
[
"Nazmul Islam",
"Seokjoo Shin"
] |
2024-07-08T22:58:11Z
|
2024-01-08T18:38:51Z
|
2407.06438
|
A Single Transformer for Scalable Vision-Language Modeling
|
We present SOLO, a single transformer for Scalable visiOn-Language mOdeling. Current large vision-language models (LVLMs) such as LLaVA mostly employ heterogeneous architectures that connect pre-trained visual encoders with large language models (LLMs) to facilitate visual recognition and complex reasoning. Although achieving remarkable performance with relatively lightweight training, we identify four primary scalability limitations: (1) The visual capacity is constrained by pre-trained visual encoders, which are typically an order of magnitude smaller than LLMs. (2) The heterogeneous architecture complicates the use of established hardware and software infrastructure. (3) Study of scaling laws on such architecture must consider three separate components - visual encoder, connector, and LLMs, which complicates the analysis. (4) The use of existing visual encoders typically requires following a pre-defined specification of image inputs pre-processing, for example, by reshaping inputs to fixed-resolution square images, which presents difficulties in processing and training on high-resolution images or those with unusual aspect ratio. A unified single Transformer architecture, like SOLO, effectively addresses these scalability concerns in LVLMs; however, its limited adoption in the modern context likely stems from the absence of reliable training recipes that balance both modalities and ensure stable training for billion-scale models. In this paper, we introduce the first open-source training recipe for developing SOLO, an open-source 7B LVLM using moderate academic resources. The training recipe involves initializing from LLMs, sequential pre-training on ImageNet and web-scale data, and instruction fine-tuning on our curated high-quality datasets. On extensive evaluation, SOLO demonstrates performance comparable to LLaVA-v1.5-7B, particularly excelling in visual mathematical reasoning.
|
http://arxiv.org/pdf/2407.06438v1
|
[
"Yangyi Chen",
"Xingyao Wang",
"Hao Peng",
"Heng Ji"
] |
2024-07-08T22:40:15Z
|
2024-07-08T22:40:15Z
|
2207.02829
|
Online Bilevel Optimization: Regret Analysis of Online Alternating
Gradient Methods
|
This paper introduces textit{online bilevel optimization} in which a sequence of time-varying bilevel problems is revealed one after the other. We extend the known regret bounds for online single-level algorithms to the bilevel setting. Specifically, we provide new notions of textit{bilevel regret}, develop an online alternating time-averaged gradient method that is capable of leveraging smoothness, and give regret bounds in terms of the path-length of the inner and outer minimizer sequences.
|
http://arxiv.org/pdf/2207.02829v7
|
[
"Davoud Ataee Tarzanagh",
"Parvin Nazari",
"Bojian Hou",
"Li Shen",
"Laura Balzano"
] |
2024-07-08T22:10:33Z
|
2022-07-06T17:36:59Z
|
2407.06418
|
System stabilization with policy optimization on unstable latent
manifolds
|
Stability is a basic requirement when studying the behavior of dynamical systems. However, stabilizing dynamical systems via reinforcement learning is challenging because only little data can be collected over short time horizons before instabilities are triggered and data become meaningless. This work introduces a reinforcement learning approach that is formulated over latent manifolds of unstable dynamics so that stabilizing policies can be trained from few data samples. The unstable manifolds are minimal in the sense that they contain the lowest dimensional dynamics that are necessary for learning policies that guarantee stabilization. This is in stark contrast to generic latent manifolds that aim to approximate all -- stable and unstable -- system dynamics and thus are higher dimensional and often require higher amounts of data. Experiments demonstrate that the proposed approach stabilizes even complex physical systems from few data samples for which other methods that operate either directly in the system state space or on generic latent manifolds fail.
|
http://arxiv.org/pdf/2407.06418v1
|
[
"Steffen W. R. Werner",
"Benjamin Peherstorfer"
] |
2024-07-08T21:57:28Z
|
2024-07-08T21:57:28Z
|
2401.10652
|
AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence
Inference
|
Large deep learning models have achieved impressive performance across a range of applications. However, their large memory requirements, including parameter memory and activation memory, have become a significant challenge for their practical serving. While existing methods mainly address parameter memory, the importance of activation memory has been overlooked. Especially for long input sequences, activation memory is expected to experience a significant exponential growth as the length of sequences increases. In this approach, we propose AutoChunk, an automatic and adaptive compiler system that efficiently reduces activation memory for long sequence inference by chunk strategies. The proposed system generates chunk plans by optimizing through multiple stages. In each stage, the chunk search pass explores all possible chunk candidates and the chunk selection pass identifies the optimal one. At runtime, AutoChunk employs code generation to automatically apply chunk strategies. The experiments demonstrate that AutoChunk can reduce over 80% of activation memory while maintaining speed loss within 10%, extend max sequence length by 3.2x to 11.7x, and outperform state-of-the-art methods by a large margin.
|
http://arxiv.org/pdf/2401.10652v3
|
[
"Xuanlei Zhao",
"Shenggan Cheng",
"Guangyang Lu",
"Jiarui Fang",
"Haotian Zhou",
"Bin Jia",
"Ziming Liu",
"Yang You"
] |
2024-07-08T21:52:08Z
|
2024-01-19T11:58:13Z
|
2304.13033
|
SmartChoices: Augmenting Software with Learned Implementations
|
In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior. While machine learning may outperform these heuristics, replacing existing heuristics in a production system safely and reliably can be prohibitively costly. We present SmartChoices, a novel approach that reduces the cost to deploy production-ready ML solutions for contextual bandits problems. SmartChoices' interface cleanly separates problem formulation from implementation details: engineers describe their use case by defining datatypes for the context, arms, and feedback that are passed to SmartChoices APIs, while SmartChoices manages encoding & logging data and training, evaluating & deploying policies. Our implementation codifies best practices, is efficient enough for use in low-level applications, and provides valuable production features off the shelf via a shared library. Overall, SmartChoices enables non-experts to rapidly deploy production-ready ML solutions by eliminating many sources of technical debt common to ML systems. Engineers have independently used SmartChoices to improve a wide range of software including caches, batch processing workloads, and UI layouts, resulting in better latency, throughput, and click-through rates.
|
http://arxiv.org/pdf/2304.13033v3
|
[
"Daniel Golovin",
"Gabor Bartok",
"Eric Chen",
"Emily Donahue",
"Tzu-Kuo Huang",
"Efi Kokiopoulou",
"Ruoyan Qin",
"Nikhil Sarda",
"Justin Sybrandt",
"Vincent Tjeng"
] |
2024-07-08T21:44:23Z
|
2023-04-12T21:55:35Z
|
2407.06411
|
If You Don't Understand It, Don't Use It: Eliminating Trojans with
Filters Between Layers
|
Large language models (LLMs) sometimes exhibit dangerous unintended behaviors. Finding and fixing these is challenging because the attack surface is massive -- it is not tractable to exhaustively search for all possible inputs that may elicit such behavior. One specific and particularly challenging case is that if data-poisoning-injected trojans, since there is no way to know what they are to search for them. To our knowledge, there is no generally applicable method to unlearn unknown trojans injected during pre-training. This work seeks to provide a general purpose recipe (filters) and a specific implementation (LoRA) filters that work in practice on small to medium sized models. The focus is primarily empirical, though some perplexing behavior opens the door to the fundamental question of how LLMs store and process information. Not unexpectedly, we find that our filters work best on the residual stream and the latest layers.
|
http://arxiv.org/pdf/2407.06411v1
|
[
"Adriano Hernandez"
] |
2024-07-08T21:40:23Z
|
2024-07-08T21:40:23Z
|
2403.07605
|
Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in
Text-To-Image Generation
|
In text-to-image generation, using negative prompts, which describe undesirable image characteristics, can significantly boost image quality. However, producing good negative prompts is manual and tedious. To address this, we propose NegOpt, a novel method for optimizing negative prompt generation toward enhanced image generation, using supervised fine-tuning and reinforcement learning. Our combined approach results in a substantial increase of 25% in Inception Score compared to other approaches and surpasses ground-truth negative prompts from the test set. Furthermore, with NegOpt we can preferentially optimize the metrics most important to us. Finally, we construct Negative Prompts DB (https://github.com/mikeogezi/negopt), a publicly available dataset of negative prompts.
|
http://arxiv.org/pdf/2403.07605v2
|
[
"Michael Ogezi",
"Ning Shi"
] |
2024-07-08T21:37:03Z
|
2024-03-12T12:44:34Z
|
2405.13712
|
Learning Diffusion Priors from Observations by Expectation Maximization
|
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present a novel method based on the expectation-maximization algorithm for training diffusion models from incomplete and noisy observations only. Unlike previous works, our method leads to proper diffusion models, which is crucial for downstream tasks. As part of our method, we propose and motivate a new posterior sampling scheme for unconditional diffusion models. We present empirical evidence supporting the effectiveness of our method.
|
http://arxiv.org/pdf/2405.13712v2
|
[
"François Rozet",
"Gérôme Andry",
"François Lanusse",
"Gilles Louppe"
] |
2024-07-08T21:12:54Z
|
2024-05-22T15:04:06Z
|
2407.06390
|
JANET: Joint Adaptive predictioN-region Estimation for Time-series
|
Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (Joint Adaptive predictioN-region Estimation for Time-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET's superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.
|
http://arxiv.org/pdf/2407.06390v1
|
[
"Eshant English",
"Eliot Wong-Toi",
"Matteo Fontana",
"Stephan Mandt",
"Padhraic Smyth",
"Christoph Lippert"
] |
2024-07-08T21:03:15Z
|
2024-07-08T21:03:15Z
|
2208.07497
|
Bucketized Active Sampling for Learning ACOPF
|
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their training requires significant data, each instance necessitating the (offline) solving of an OPF. To meet the requirements of market-clearing applications, this paper proposes Bucketized Active Sampling (BAS), a novel active learning framework that aims at training the best possible OPF proxy within a time limit. BAS partitions the input domain into buckets and uses an acquisition function to determine where to sample next. By applying the same partitioning to the validation set, BAS leverages labeled validation samples in the selection of unlabeled samples. BAS also relies on an adaptive learning rate that increases and decreases over time. Experimental results demonstrate the benefits of BAS.
|
http://arxiv.org/abs/2208.07497v3
|
[
"Michael Klamkin",
"Mathieu Tanneau",
"Terrence W. K. Mak",
"Pascal Van Hentenryck"
] |
2024-07-08T21:00:14Z
|
2022-08-16T02:04:17Z
|
2401.17263
|
Robust Prompt Optimization for Defending Language Models Against
Jailbreaking Attacks
|
Despite advances in AI alignment, large language models (LLMs) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries can modify prompts to induce unwanted behavior. While some defenses have been proposed, they have not been adapted to newly proposed attacks and more challenging threat models. To address this, we propose an optimization-based objective for defending LLMs against jailbreaking attacks and an algorithm, Robust Prompt Optimization (RPO) to create robust system-level defenses. Our approach directly incorporates the adversary into the defensive objective and optimizes a lightweight and transferable suffix, enabling RPO to adapt to worst-case adaptive attacks. Our theoretical and experimental results show improved robustness to both jailbreaks seen during optimization and unknown jailbreaks, reducing the attack success rate (ASR) on GPT-4 to 6% and Llama-2 to 0% on JailbreakBench, setting the state-of-the-art. Code can be found at https://github.com/lapisrocks/rpo
|
http://arxiv.org/pdf/2401.17263v4
|
[
"Andy Zhou",
"Bo Li",
"Haohan Wang"
] |
2024-07-08T20:33:36Z
|
2024-01-30T18:56:08Z
|
2407.06372
|
Non-Robust Features are Not Always Useful in One-Class Classification
|
The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that require lightweight models for one-class classification. Building on Ilyas et al. (2019), we investigate the vulnerability of lightweight one-class classifiers to adversarial attacks and possible reasons for it. Our results show that lightweight one-class classifiers learn features that are not robust (e.g. texture) under stronger attacks. However, unlike in multi-class classification (Ilyas et al., 2019), these non-robust features are not always useful for the one-class task, suggesting that learning these unpredictive and non-robust features is an unwanted consequence of training.
|
http://arxiv.org/pdf/2407.06372v1
|
[
"Matthew Lau",
"Haoran Wang",
"Alec Helbling",
"Matthew Hul",
"ShengYun Peng",
"Martin Andreoni",
"Willian T. Lunardi",
"Wenke Lee"
] |
2024-07-08T20:32:19Z
|
2024-07-08T20:32:19Z
|
2302.02162
|
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks
against Interpretable Models
|
Explainable Artificial Intelligence (XAI) aims to uncover the decision-making processes of AI models. However, the data used for such explanations can pose security and privacy risks. Existing literature identifies attacks on machine learning models, including membership inference, model inversion, and model extraction attacks. These attacks target either the model or the training data, depending on the settings and parties involved. XAI tools can increase the vulnerability of model extraction attacks, which is a concern when model owners prefer black-box access, thereby keeping model parameters and architecture private. To exploit this risk, we propose AUTOLYCUS, a novel retraining (learning) based model extraction attack framework against interpretable models under black-box settings. As XAI tools, we exploit Local Interpretable Model-Agnostic Explanations (LIME) and Shapley values (SHAP) to infer decision boundaries and create surrogate models that replicate the functionality of the target model. LIME and SHAP are mainly chosen for their realistic yet information-rich explanations, coupled with their extensive adoption, simplicity, and usability. We evaluate AUTOLYCUS on six machine learning datasets, measuring the accuracy and similarity of the surrogate model to the target model. The results show that AUTOLYCUS is highly effective, requiring significantly fewer queries compared to state-of-the-art attacks, while maintaining comparable accuracy and similarity. We validate its performance and transferability on multiple interpretable ML models, including decision trees, logistic regression, naive bayes, and k-nearest neighbor. Additionally, we show the resilience of AUTOLYCUS against proposed countermeasures.
|
http://arxiv.org/abs/2302.02162v3
|
[
"Abdullah Caglar Oksuz",
"Anisa Halimi",
"Erman Ayday"
] |
2024-07-08T20:17:23Z
|
2023-02-04T13:23:39Z
|
2407.00324
|
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning
|
Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible. These problems, when formulated as episodic reinforcement learning tasks, can easily be specified to align well with our intended goal: -1 reward every time step with termination upon reaching the goal state, called minimum-time tasks. Despite this simplicity, such formulations are often overlooked in favor of dense rewards due to their perceived difficulty and lack of informativeness. Our studies contrast the two reward paradigms, revealing that the minimum-time task specification not only facilitates learning higher-quality policies but can also surpass dense-reward-based policies on their own performance metrics. Crucially, we also identify the goal-hit rate of the initial policy as a robust early indicator for learning success in such sparse feedback settings. Finally, using four distinct real-robotic platforms, we show that it is possible to learn pixel-based policies from scratch within two to three hours using constant negative rewards.
|
http://arxiv.org/pdf/2407.00324v2
|
[
"Gautham Vasan",
"Yan Wang",
"Fahim Shahriar",
"James Bergstra",
"Martin Jagersand",
"A. Rupam Mahmood"
] |
2024-07-08T20:15:46Z
|
2024-06-29T05:55:33Z
|
2406.15487
|
Improving Text-To-Audio Models with Synthetic Captions
|
It is an open challenge to obtain high quality training data, especially captions, for text-to-audio models. Although prior methods have leveraged textit{text-only language models} to augment and improve captions, such methods have limitations related to scale and coherence between audio and captions. In this work, we propose an audio captioning pipeline that uses an textit{audio language model} to synthesize accurate and diverse captions for audio at scale. We leverage this pipeline to produce a dataset of synthetic captions for AudioSet, named texttt{AF-AudioSet}, and then evaluate the benefit of pre-training text-to-audio models on these synthetic captions. Through systematic evaluations on AudioCaps and MusicCaps, we find leveraging our pipeline and synthetic captions leads to significant improvements on audio generation quality, achieving a new textit{state-of-the-art}.
|
http://arxiv.org/pdf/2406.15487v2
|
[
"Zhifeng Kong",
"Sang-gil Lee",
"Deepanway Ghosal",
"Navonil Majumder",
"Ambuj Mehrish",
"Rafael Valle",
"Soujanya Poria",
"Bryan Catanzaro"
] |
2024-07-08T20:15:33Z
|
2024-06-18T00:02:15Z
|
2308.02040
|
Learning Regionalization using Accurate Spatial Cost Gradients within a
Differentiable High-Resolution Hydrological Model: Application to the French
Mediterranean Region
|
Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on either multi-linear regressions or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous datasets across extensive spatio-temporal computational domains within a high-dimensional regionalization context, using accurate adjoint-based gradients. The inverse problem is tackled with a multi-gauge calibration cost function accounting for information from multiple observation sites. HDA-PR was tested on high-resolution, hourly and kilometric regional modeling of 126 flash-flood-prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA-PR especially in the most challenging upstream-to-downstream extrapolation scenario with ANN, achieving median Nash-Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio-temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. ANN enables to learn a non-linear descriptors-to-parameters mapping which provides better model controllability than a linear mapping for complex calibration cases.
|
http://arxiv.org/pdf/2308.02040v2
|
[
"Ngo Nghi Truyen Huynh",
"Pierre-André Garambois",
"François Colleoni",
"Benjamin Renard",
"Hélène Roux",
"Julie Demargne",
"Maxime Jay-Allemand",
"Pierre Javelle"
] |
2024-07-08T20:08:43Z
|
2023-08-02T07:23:50Z
|
2303.02444
|
Calibrating Transformers via Sparse Gaussian Processes
|
Transformer models have achieved profound success in prediction tasks in a wide range of applications in natural language processing, speech recognition and computer vision. Extending Transformer's success to safety-critical domains requires calibrated uncertainty estimation which remains under-explored. To address this, we propose Sparse Gaussian Process attention (SGPA), which performs Bayesian inference directly in the output space of multi-head attention blocks (MHAs) in transformer to calibrate its uncertainty. It replaces the scaled dot-product operation with a valid symmetric kernel and uses sparse Gaussian processes (SGP) techniques to approximate the posterior processes of MHA outputs. Empirically, on a suite of prediction tasks on text, images and graphs, SGPA-based Transformers achieve competitive predictive accuracy, while noticeably improving both in-distribution calibration and out-of-distribution robustness and detection.
|
http://arxiv.org/pdf/2303.02444v3
|
[
"Wenlong Chen",
"Yingzhen Li"
] |
2024-07-08T19:56:35Z
|
2023-03-04T16:04:17Z
|
2302.07950
|
Self-Organising Neural Discrete Representation Learning à la Kohonen
|
Unsupervised learning of discrete representations in neural networks (NNs) from continuous ones is essential for many modern applications. Vector Quantisation (VQ) has become popular for this, in particular in the context of generative models, such as Variational Auto-Encoders (VAEs), where the exponential moving average-based VQ (EMA-VQ) algorithm is often used. Here, we study an alternative VQ algorithm based on Kohonen's learning rule for the Self-Organising Map (KSOM; 1982). EMA-VQ is a special case of KSOM. KSOM is known to offer two potential benefits: empirically, it converges faster than EMA-VQ, and KSOM-generated discrete representations form a topological structure on the grid whose nodes are the discrete symbols, resulting in an artificial version of the brain's topographic map. We revisit these properties by using KSOM in VQ-VAEs for image processing. In our experiments, the speed-up compared to well-configured EMA-VQ is only observable at the beginning of training, but KSOM is generally much more robust, e.g., w.r.t. the choice of initialisation schemes.
|
http://arxiv.org/pdf/2302.07950v2
|
[
"Kazuki Irie",
"Róbert Csordás",
"Jürgen Schmidhuber"
] |
2024-07-08T19:47:40Z
|
2023-02-15T21:04:04Z
|
2407.06346
|
High-Dimensional Distributed Sparse Classification with Scalable
Communication-Efficient Global Updates
|
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and exploit parallelism to reduce memory and runtime, but suffer increasingly from communication costs as the data size or the number of iterations grows. Recent work on linear models has shown that a surrogate likelihood can be optimized locally to iteratively improve on an initial solution in a communication-efficient manner. However, existing versions of these methods experience multiple shortcomings as the data size becomes massive, including diverging updates and efficiently handling sparsity. In this work we develop solutions to these problems which enable us to learn a communication-efficient distributed logistic regression model even beyond millions of features. In our experiments we demonstrate a large improvement in accuracy over distributed algorithms with only a few distributed update steps needed, and similar or faster runtimes. Our code is available at url{https://github.com/FutureComputing4AI/ProxCSL}.
|
http://arxiv.org/abs/2407.06346v1
|
[
"Fred Lu",
"Ryan R. Curtin",
"Edward Raff",
"Francis Ferraro",
"James Holt"
] |
2024-07-08T19:34:39Z
|
2024-07-08T19:34:39Z
|
2306.08754
|
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid
ML-physics Climate Emulation
|
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid ML-physics simulations require domain-specific data and workflows that have been inaccessible to many ML experts. As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various ML baselines, alongside a hybrid baseline simulator, to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid ML-physics and high-fidelity climate simulations.
|
http://arxiv.org/pdf/2306.08754v6
|
[
"Sungduk Yu",
"Zeyuan Hu",
"Akshay Subramaniam",
"Walter Hannah",
"Liran Peng",
"Jerry Lin",
"Mohamed Aziz Bhouri",
"Ritwik Gupta",
"Björn Lütjens",
"Justus C. Will",
"Gunnar Behrens",
"Julius J. M. Busecke",
"Nora Loose",
"Charles I. Stern",
"Tom Beucler",
"Bryce Harrop",
"Helge Heuer",
"Benjamin R. Hillman",
"Andrea Jenney",
"Nana Liu",
"Alistair White",
"Tian Zheng",
"Zhiming Kuang",
"Fiaz Ahmed",
"Elizabeth Barnes",
"Noah D. Brenowitz",
"Christopher Bretherton",
"Veronika Eyring",
"Savannah Ferretti",
"Nicholas Lutsko",
"Pierre Gentine",
"Stephan Mandt",
"J. David Neelin",
"Rose Yu",
"Laure Zanna",
"Nathan Urban",
"Janni Yuval",
"Ryan Abernathey",
"Pierre Baldi",
"Wayne Chuang",
"Yu Huang",
"Fernando Iglesias-Suarez",
"Sanket Jantre",
"Po-Lun Ma",
"Sara Shamekh",
"Guang Zhang",
"Michael Pritchard"
] |
2024-07-08T19:33:54Z
|
2023-06-14T21:26:31Z
|
2407.06343
|
Novel Models for High-Dimensional Imaging: High-Resolution fMRI
Acceleration and Quantification
|
The goals of functional Magnetic Resonance Imaging (fMRI) include high spatial and temporal resolutions with a high signal-to-noise ratio (SNR). To simultaneously improve spatial and temporal resolutions and maintain the high SNR advantage of OSSI, we present novel pipelines for fast acquisition and high-resolution fMRI reconstruction and physics parameter quantification. We propose a patch-tensor low-rank model, a physics-based manifold model, and a voxel-wise attention network. With novel models for acquisition and reconstruction, we demonstrate that we can improve SNR and resolution simultaneously without compromising scan time. All the proposed models outperform other comparison approaches with higher resolution and more functional information.
|
http://arxiv.org/pdf/2407.06343v1
|
[
"Shouchang Guo"
] |
2024-07-08T19:24:21Z
|
2024-07-08T19:24:21Z
|
2312.10293
|
Inductive Link Prediction in Knowledge Graphs using Path-based Neural
Networks
|
Link prediction is a crucial research area in knowledge graphs, with many downstream applications. In many real-world scenarios, inductive link prediction is required, where predictions have to be made among unseen entities. Embedding-based models usually need fine-tuning on new entity embeddings, and hence are difficult to be directly applied to inductive link prediction tasks. Logical rules captured by rule-based models can be directly applied to new entities with the same graph typologies, but the captured rules are discrete and usually lack generosity. Graph neural networks (GNNs) can generalize topological information to new graphs taking advantage of deep neural networks, which however may still need fine-tuning on new entity embeddings. In this paper, we propose SiaILP, a path-based model for inductive link prediction using siamese neural networks. Our model only depends on relation and path embeddings, which can be generalized to new entities without fine-tuning. Experiments show that our model achieves several new state-of-the-art performances in link prediction tasks using inductive versions of WN18RR, FB15k-237, and Nell995. Our code is available at url{https://github.com/canlinzhang/SiaILP}.
|
http://arxiv.org/pdf/2312.10293v2
|
[
"Canlin Zhang",
"Xiuwen Liu"
] |
2024-07-08T19:01:47Z
|
2023-12-16T02:26:09Z
|
2407.06329
|
Solving Multi-Model MDPs by Coordinate Ascent and Dynamic Programming
|
Multi-model Markov decision process (MMDP) is a promising framework for computing policies that are robust to parameter uncertainty in MDPs. MMDPs aim to find a policy that maximizes the expected return over a distribution of MDP models. Because MMDPs are NP-hard to solve, most methods resort to approximations. In this paper, we derive the policy gradient of MMDPs and propose CADP, which combines a coordinate ascent method and a dynamic programming algorithm for solving MMDPs. The main innovation of CADP compared with earlier algorithms is to take the coordinate ascent perspective to adjust model weights iteratively to guarantee monotone policy improvements to a local maximum. A theoretical analysis of CADP proves that it never performs worse than previous dynamic programming algorithms like WSU. Our numerical results indicate that CADP substantially outperforms existing methods on several benchmark problems.
|
http://arxiv.org/pdf/2407.06329v1
|
[
"Xihong Su",
"Marek Petrik"
] |
2024-07-08T18:47:59Z
|
2024-07-08T18:47:59Z
|
2407.06325
|
CONGO: Compressive Online Gradient Optimization with Application to
Microservices Management
|
We address the challenge of online convex optimization where the objective function's gradient exhibits sparsity, indicating that only a small number of dimensions possess non-zero gradients. Our aim is to leverage this sparsity to obtain useful estimates of the objective function's gradient even when the only information available is a limited number of function samples. Our motivation stems from distributed queueing systems like microservices-based applications, characterized by request-response workloads. Here, each request type proceeds through a sequence of microservices to produce a response, and the resource allocation across the collection of microservices is controlled to balance end-to-end latency with resource costs. While the number of microservices is substantial, the latency function primarily reacts to resource changes in a few, rendering the gradient sparse. Our proposed method, CONGO (Compressive Online Gradient Optimization), combines simultaneous perturbation with compressive sensing to estimate gradients. We establish analytical bounds on the requisite number of compressive sensing samples per iteration to maintain bounded bias of gradient estimates, ensuring sub-linear regret. By exploiting sparsity, we reduce the samples required per iteration to match the gradient's sparsity, rather than the problem's original dimensionality. Numerical experiments and real-world microservices benchmarks demonstrate CONGO's superiority over multiple stochastic gradient descent approaches, as it quickly converges to performance comparable to policies pre-trained with workload awareness.
|
http://arxiv.org/pdf/2407.06325v1
|
[
"Jeremy Carleton",
"Prathik Vijaykumar",
"Divyanshu Saxena",
"Dheeraj Narasimha",
"Srinivas Shakkottai",
"Aditya Akella"
] |
2024-07-08T18:42:50Z
|
2024-07-08T18:42:50Z
|
2407.06324
|
B'MOJO: Hybrid State Space Realizations of Foundation Models with
Eidetic and Fading Memory
|
We describe a family of architectures to support transductive inference by allowing memory to grow to a finite but a-priori unknown bound while making efficient use of finite resources for inference. Current architectures use such resources to represent data either eidetically over a finite span ("context" in Transformers), or fading over an infinite span (in State Space Models, or SSMs). Recent hybrid architectures have combined eidetic and fading memory, but with limitations that do not allow the designer or the learning process to seamlessly modulate the two, nor to extend the eidetic memory span. We leverage ideas from Stochastic Realization Theory to develop a class of models called B'MOJO to seamlessly combine eidetic and fading memory within an elementary composable module. The overall architecture can be used to implement models that can access short-term eidetic memory "in-context," permanent structural memory "in-weights," fading memory "in-state," and long-term eidetic memory "in-storage" by natively incorporating retrieval from an asynchronously updated memory. We show that Transformers, existing SSMs such as Mamba, and hybrid architectures such as Jamba are special cases of B'MOJO and describe a basic implementation, to be open sourced, that can be stacked and scaled efficiently in hardware. We test B'MOJO on transductive inference tasks, such as associative recall, where it outperforms existing SSMs and Hybrid models; as a baseline, we test ordinary language modeling where B'MOJO achieves perplexity comparable to similarly-sized Transformers and SSMs up to 1.4B parameters, while being up to 10% faster to train. Finally, we show that B'MOJO's ability to modulate eidetic and fading memory results in better inference on longer sequences tested up to 32K tokens, four-fold the length of the longest sequences seen during training.
|
http://arxiv.org/pdf/2407.06324v1
|
[
"Luca Zancato",
"Arjun Seshadri",
"Yonatan Dukler",
"Aditya Golatkar",
"Yantao Shen",
"Benjamin Bowman",
"Matthew Trager",
"Alessandro Achille",
"Stefano Soatto"
] |
2024-07-08T18:41:01Z
|
2024-07-08T18:41:01Z
|
2407.06322
|
MagMax: Leveraging Model Merging for Seamless Continual Learning
|
This paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forgetting previously acquired knowledge. Distinct from traditional continual learning methods that aim to reduce forgetting during task training, MagMax combines sequential fine-tuning with a maximum magnitude weight selection for effective knowledge integration across tasks. Our initial contribution is an extensive examination of model merging techniques, revealing that simple approaches like weight averaging and random weight selection surprisingly hold up well in various continual learning contexts. More importantly, we present MagMax, a novel model-merging strategy that enables continual learning of large pre-trained models for successive tasks. Our thorough evaluation demonstrates the superiority of MagMax in various scenarios, including class- and domain-incremental learning settings.
|
http://arxiv.org/pdf/2407.06322v1
|
[
"Daniel Marczak",
"Bartłomiej Twardowski",
"Tomasz Trzciński",
"Sebastian Cygert"
] |
2024-07-08T18:38:52Z
|
2024-07-08T18:38:52Z
|
2405.02154
|
Neural Context Flows for Learning Generalizable Dynamical Systems
|
Neural Ordinary Differential Equations typically struggle to generalize to new dynamical behaviors created by parameter changes in the underlying system, even when the dynamics are close to previously seen behaviors. The issue gets worse when the changing parameters are unobserved, i.e., their value or influence is not directly measurable when collecting data. We introduce Neural Context Flow (NCF), a framework that encodes said unobserved parameters in a latent context vector as input to a vector field. NCFs leverage differentiability of the vector field with respect to the parameters, along with first-order Taylor expansion to allow any context vector to influence trajectories from other parameters. We validate our method and compare it to established Multi-Task and Meta-Learning alternatives, showing competitive performance in mean squared error for in-domain and out-of-distribution evaluation on the Lotka-Volterra, Glycolytic Oscillator, and Gray-Scott problems. This study holds practical implications for foundational models in science and related areas that benefit from conditional neural ODEs. Our code is openly available at https://github.com/ddrous/ncflow.
|
http://arxiv.org/pdf/2405.02154v2
|
[
"Roussel Desmond Nzoyem",
"David A. W. Barton",
"Tom Deakin"
] |
2024-07-08T18:38:41Z
|
2024-05-03T15:02:21Z
|
2407.06321
|
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with
Bernoulli Rewards
|
We consider Kernelized Bandits (KBs) to optimize a function $f : mathcal{X} rightarrow [0,1]$ belonging to the Reproducing Kernel Hilbert Space (RKHS) $mathcal{H}_k$. Mainstream works on kernelized bandits focus on a subgaussian noise model in which observations of the form $f(mathbf{x}_t)+epsilon_t$, being $epsilon_t$ a subgaussian noise, are available (Chowdhury and Gopalan, 2017). Differently, we focus on the case in which we observe realizations $y_t sim text{Ber}(f(mathbf{x}_t))$ sampled from a Bernoulli distribution with parameter $f(mathbf{x}_t)$. While the Bernoulli model has been investigated successfully in multi-armed bandits (Garivier and Capp'e, 2011), logistic bandits (Faury et al., 2022), bandits in metric spaces (Magureanu et al., 2014), it remains an open question whether tight results can be obtained for KBs. This paper aims to draw the attention of the online learning community to this open problem.
|
http://arxiv.org/pdf/2407.06321v1
|
[
"Marco Mussi",
"Simone Drago",
"Alberto Maria Metelli"
] |
2024-07-08T18:38:11Z
|
2024-07-08T18:38:11Z
|
2407.06312
|
Limits and Powers of Koopman Learning
|
Dynamical systems provide a comprehensive way to study complex and changing behaviors across various sciences. Many modern systems are too complicated to analyze directly or we do not have access to models, driving significant interest in learning methods. Koopman operators have emerged as a dominant approach because they allow the study of nonlinear dynamics using linear techniques by solving an infinite-dimensional spectral problem. However, current algorithms face challenges such as lack of convergence, hindering practical progress. This paper addresses a fundamental open question: textit{When can we robustly learn the spectral properties of Koopman operators from trajectory data of dynamical systems, and when can we not?} Understanding these boundaries is crucial for analysis, applications, and designing algorithms. We establish a foundational approach that combines computational analysis and ergodic theory, revealing the first fundamental barriers -- universal for any algorithm -- associated with system geometry and complexity, regardless of data quality and quantity. For instance, we demonstrate well-behaved smooth dynamical systems on tori where non-trivial eigenfunctions of the Koopman operator cannot be determined by any sequence of (even randomized) algorithms, even with unlimited training data. Additionally, we identify when learning is possible and introduce optimal algorithms with verification that overcome issues in standard methods. These results pave the way for a sharp classification theory of data-driven dynamical systems based on how many limits are needed to solve a problem. These limits characterize all previous methods, presenting a unified view. Our framework systematically determines when and how Koopman spectral properties can be learned.
|
http://arxiv.org/pdf/2407.06312v1
|
[
"Matthew J. Colbrook",
"Igor Mezić",
"Alexei Stepanenko"
] |
2024-07-08T18:24:48Z
|
2024-07-08T18:24:48Z
|
2407.06310
|
Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly
Speaker Adaptation
|
The application of data-intensive automatic speech recognition (ASR) technologies to dysarthric and elderly adult speech is confronted by their mismatch against healthy and nonaged voices, data scarcity and large speaker-level variability. To this end, this paper proposes two novel data-efficient methods to learn homogeneous dysarthric and elderly speaker-level features for rapid, on-the-fly test-time adaptation of DNN/TDNN and Conformer ASR models. These include: 1) speaker-level variance-regularized spectral basis embedding (VR-SBE) features that exploit a special regularization term to enforce homogeneity of speaker features in adaptation; and 2) feature-based learning hidden unit contributions (f-LHUC) transforms that are conditioned on VR-SBE features. Experiments are conducted on four tasks across two languages: the English UASpeech and TORGO dysarthric speech datasets, the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech corpora. The proposed on-the-fly speaker adaptation techniques consistently outperform baseline iVector and xVector adaptation by statistically significant word or character error rate reductions up to 5.32% absolute (18.57% relative) and batch-mode LHUC speaker adaptation by 2.24% absolute (9.20% relative), while operating with real-time factors speeding up to 33.6 times against xVectors during adaptation. The efficacy of the proposed adaptation techniques is demonstrated in a comparison against current ASR technologies including SSL pre-trained systems on UASpeech, where our best system produces a state-of-the-art WER of 23.33%. Analyses show VR-SBE features and f-LHUC transforms are insensitive to speaker-level data quantity in testtime adaptation. T-SNE visualization reveals they have stronger speaker-level homogeneity than baseline iVectors, xVectors and batch-mode LHUC transforms.
|
http://arxiv.org/pdf/2407.06310v1
|
[
"Mengzhe Geng",
"Xurong Xie",
"Jiajun Deng",
"Zengrui Jin",
"Guinan Li",
"Tianzi Wang",
"Shujie Hu",
"Zhaoqing Li",
"Helen Meng",
"Xunying Liu"
] |
2024-07-08T18:20:24Z
|
2024-07-08T18:20:24Z
|
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