categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.16640
| null | null |
http://arxiv.org/pdf/2403.16640v1
|
2024-03-25T11:28:52Z
|
2024-03-25T11:28:52Z
|
Multi-Scale Texture Loss for CT denoising with GANs
|
Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM). Although the recent advances in deep learning have demonstrated superior performance in classification and detection tasks, we hypothesize that its information content can be valuable when integrated into GANs' training. To this end, we propose a differentiable implementation of the GLCM suited for gradient-based optimization. Our approach also introduces a self-attention layer that dynamically aggregates the multi-scale texture information extracted from the images. We validate our approach by carrying out extensive experiments in the context of low-dose CT denoising, a challenging application that aims to enhance the quality of noisy CT scans. We utilize three publicly available datasets, including one simulated and two real datasets. The results are promising as compared to other well-established loss functions, being also consistent across three different GAN architectures. The code is available at: https://github.com/FrancescoDiFeola/DenoTextureLoss
|
[
"['Francesco Di Feola' 'Lorenzo Tronchin' 'Valerio Guarrasi' 'Paolo Soda']"
] |
null | null |
2403.16644
| null | null |
http://arxiv.org/pdf/2403.16644v1
|
2024-03-25T11:29:32Z
|
2024-03-25T11:29:32Z
|
Bridging the Sim-to-Real Gap with Bayesian Inference
|
We present SIM-FSVGD for learning robot dynamics from data. As opposed to traditional methods, SIM-FSVGD leverages low-fidelity physical priors, e.g., in the form of simulators, to regularize the training of neural network models. While learning accurate dynamics already in the low data regime, SIM-FSVGD scales and excels also when more data is available. We empirically show that learning with implicit physical priors results in accurate mean model estimation as well as precise uncertainty quantification. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system. Using model-based RL, we demonstrate a highly dynamic parking maneuver with drifting, using less than half the data compared to the state of the art.
|
[
"['Jonas Rothfuss' 'Bhavya Sukhija' 'Lenart Treven' 'Florian Dörfler'\n 'Stelian Coros' 'Andreas Krause']"
] |
null | null |
2403.16654
| null | null |
http://arxiv.org/pdf/2403.16654v1
|
2024-03-25T11:42:01Z
|
2024-03-25T11:42:01Z
|
A Novel Loss Function-based Support Vector Machine for Binary
Classification
|
The previous support vector machine(SVM) including $0/1$ loss SVM, hinge loss SVM, ramp loss SVM, truncated pinball loss SVM, and others, overlooked the degree of penalty for the correctly classified samples within the margin. This oversight affects the generalization ability of the SVM classifier to some extent. To address this limitation, from the perspective of confidence margin, we propose a novel Slide loss function ($ell_s$) to construct the support vector machine classifier($ell_s$-SVM). By introducing the concept of proximal stationary point, and utilizing the property of Lipschitz continuity, we derive the first-order optimality conditions for $ell_s$-SVM. Based on this, we define the $ell_s$ support vectors and working set of $ell_s$-SVM. To efficiently handle $ell_s$-SVM, we devise a fast alternating direction method of multipliers with the working set ($ell_s$-ADMM), and provide the convergence analysis. The numerical experiments on real world datasets confirm the robustness and effectiveness of the proposed method.
|
[
"['Yan Li' 'Liping Zhang']"
] |
null | null |
2403.16656
| null | null |
http://arxiv.org/pdf/2403.16656v1
|
2024-03-25T11:47:53Z
|
2024-03-25T11:47:53Z
|
Graph Augmentation for Recommendation
|
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing GCL models to real-world recommendation environments poses challenges. There are two primary issues to address. Firstly, the lack of consideration for data noise in contrastive learning can result in noisy self-supervised signals, leading to degraded performance. Secondly, many existing GCL approaches rely on graph neural network (GNN) architectures, which can suffer from over-smoothing problems due to non-adaptive message passing. To address these challenges, we propose a principled framework called GraphAug. This framework introduces a robust data augmentor that generates denoised self-supervised signals, enhancing recommender systems. The GraphAug framework incorporates a graph information bottleneck (GIB)-regularized augmentation paradigm, which automatically distills informative self-supervision information and adaptively adjusts contrastive view generation. Through rigorous experimentation on real-world datasets, we thoroughly assessed the performance of our novel GraphAug model. The outcomes consistently unveil its superiority over existing baseline methods. The source code for our model is publicly available at: https://github.com/HKUDS/GraphAug.
|
[
"['Qianru Zhang' 'Lianghao Xia' 'Xuheng Cai' 'Siuming Yiu' 'Chao Huang'\n 'Christian S. Jensen']"
] |
null | null |
2403.16674
| null | null |
http://arxiv.org/pdf/2403.16674v1
|
2024-03-25T12:13:20Z
|
2024-03-25T12:13:20Z
|
Understanding the Functional Roles of Modelling Components in Spiking
Neural Networks
|
Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models.
|
[
"['Huifeng Yin' 'Hanle Zheng' 'Jiayi Mao' 'Siyuan Ding' 'Xing Liu'\n 'Mingkun Xu' 'Yifan Hu' 'Jing Pei' 'Lei Deng']"
] |
null | null |
2403.16677
| null | null |
http://arxiv.org/pdf/2403.16677v1
|
2024-03-25T12:14:48Z
|
2024-03-25T12:14:48Z
|
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with
Neural Feature Compression
|
Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, network contention poses a downlink bottleneck. Orbital Edge Computing (OEC) leverages limited onboard compute resources to reduce transfer costs by processing the raw captures at the source. However, current solutions have limited practicability due to reliance on crude filtering methods or over-prioritizing particular downstream tasks. This work presents FOOL, an OEC-native and task-agnostic feature compression method that preserves prediction performance. FOOL partitions high-resolution satellite imagery to maximize throughput. Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead. While FOOL is a feature compressor, it can recover images with competitive scores on perceptual quality measures at lower bitrates. We extensively evaluate transfer cost reduction by including the peculiarity of intermittently available network connections in low earth orbit. Lastly, we test the feasibility of our system for standardized nanosatellite form factors. We demonstrate that FOOL permits downlinking over 100x the data volume without relying on prior information on the downstream tasks.
|
[
"['Alireza Furutanpey' 'Qiyang Zhang' 'Philipp Raith' 'Tobias Pfandzelter'\n 'Shangguang Wang' 'Schahram Dustdar']"
] |
null | null |
2403.16678
| null | null |
http://arxiv.org/pdf/2403.16678v1
|
2024-03-25T12:15:42Z
|
2024-03-25T12:15:42Z
|
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer
using Deep Neural Networks
|
Advances in digital pathology and artificial intelligence (AI) offer promising opportunities for clinical decision support and enhancing diagnostic workflows. Previous studies already demonstrated AI's potential for automated Gleason grading, but lack state-of-the-art methodology and model reusability. To address this issue, we propose DeepGleason: an open-source deep neural network based image classification system for automated Gleason grading using whole-slide histopathology images from prostate tissue sections. Implemented with the standardized AUCMEDI framework, our tool employs a tile-wise classification approach utilizing fine-tuned image preprocessing techniques in combination with a ConvNeXt architecture which was compared to various state-of-the-art architectures. The neural network model was trained and validated on an in-house dataset of 34,264 annotated tiles from 369 prostate carcinoma slides. We demonstrated that DeepGleason is capable of highly accurate and reliable Gleason grading with a macro-averaged F1-score of 0.806, AUC of 0.991, and Accuracy of 0.974. The internal architecture comparison revealed that the ConvNeXt model was superior performance-wise on our dataset to established and other modern architectures like transformers. Furthermore, we were able to outperform the current state-of-the-art in tile-wise fine-classification with a sensitivity and specificity of 0.94 and 0.98 for benign vs malignant detection as well as of 0.91 and 0.75 for Gleason 3 vs Gleason 4 & 5 classification, respectively. Our tool contributes to the wider adoption of AI-based Gleason grading within the research community and paves the way for broader clinical application of deep learning models in digital pathology. DeepGleason is open-source and publicly available for research application in the following Git repository: https://github.com/frankkramer-lab/DeepGleason.
|
[
"['Dominik Müller' 'Philip Meyer' 'Lukas Rentschler' 'Robin Manz'\n 'Jonas Bäcker' 'Samantha Cramer' 'Christoph Wengenmayr' 'Bruno Märkl'\n 'Ralf Huss' 'Iñaki Soto-Rey' 'Johannes Raffler']"
] |
null | null |
2403.16680
| null | null |
http://arxiv.org/pdf/2403.16680v1
|
2024-03-25T12:15:47Z
|
2024-03-25T12:15:47Z
|
Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
|
Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics. Classic numerical solvers have traditionally been computationally expensive and challenging to use in inverse problems, whereas Neural solvers aim to address both concerns through machine learning. We propose a general formulation for continuous convolutions using separable basis functions as a superset of existing methods and evaluate a large set of basis functions in the context of (a) a compressible 1D SPH simulation, (b) a weakly compressible 2D SPH simulation, and (c) an incompressible 2D SPH Simulation. We demonstrate that even and odd symmetries included in the basis functions are key aspects of stability and accuracy. Our broad evaluation shows that Fourier-based continuous convolutions outperform all other architectures regarding accuracy and generalization. Finally, using these Fourier-based networks, we show that prior inductive biases, such as window functions, are no longer necessary. An implementation of our approach, as well as complete datasets and solver implementations, is available at https://github.com/tum-pbs/SFBC.
|
[
"['Rene Winchenbach' 'Nils Thuerey']"
] |
null | null |
2403.16681
| null | null |
http://arxiv.org/pdf/2403.16681v1
|
2024-03-25T12:15:55Z
|
2024-03-25T12:15:55Z
|
A note on generalization bounds for losses with finite moments
|
This paper studies the truncation method from Alquier [1] to derive high-probability PAC-Bayes bounds for unbounded losses with heavy tails. Assuming that the $p$-th moment is bounded, the resulting bounds interpolate between a slow rate $1 / sqrt{n}$ when $p=2$, and a fast rate $1 / n$ when $p to infty$ and the loss is essentially bounded. Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance. This bound has an exponentially better dependence on the confidence parameter and the dependency measure than previous bounds in the literature. Finally, the paper extends all results to guarantees in expectation and single-draw PAC-Bayes. In order to so, it obtains analogues of the PAC-Bayes fast rate bound for bounded losses from [2] in these settings.
|
[
"['Borja Rodríguez-Gálvez' 'Omar Rivasplata' 'Ragnar Thobaben'\n 'Mikael Skoglund']"
] |
null | null |
2403.16689
| null | null |
http://arxiv.org/pdf/2403.16689v2
|
2024-05-07T02:47:55Z
|
2024-03-25T12:23:39Z
|
Synapse: Learning Preferential Concepts from Visual Demonstrations
|
This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e.g., "good parking spot", "convenient drop-off location") from visual input. Despite its similarity to learning factual concepts (e.g., "red cube"), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a new framework called Synapse, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited demonstrations. Synapse represents preferences as neuro-symbolic programs in a domain-specific language (DSL) that operates over images, and leverages a novel combination of visual parsing, large language models, and program synthesis to learn programs representing individual preferences. We evaluate Synapse through extensive experimentation including a user case study focusing on mobility-related concepts in mobile robotics and autonomous driving. Our evaluation demonstrates that Synapse significantly outperforms existing baselines as well as its own ablations. The code and other details can be found on the project website https://amrl.cs.utexas.edu/synapse .
|
[
"['Sadanand Modak' 'Noah Patton' 'Isil Dillig' 'Joydeep Biswas']"
] |
null | null |
2403.16695
| null | null |
http://arxiv.org/pdf/2403.16695v1
|
2024-03-25T12:26:32Z
|
2024-03-25T12:26:32Z
|
Assessing the Performance of Deep Learning for Automated Gleason Grading
in Prostate Cancer
|
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.
|
[
"['Dominik Müller' 'Philip Meyer' 'Lukas Rentschler' 'Robin Manz'\n 'Daniel Hieber' 'Jonas Bäcker' 'Samantha Cramer' 'Christoph Wengenmayr'\n 'Bruno Märkl' 'Ralf Huss' 'Frank Kramer' 'Iñaki Soto-Rey'\n 'Johannes Raffler']"
] |
null | null |
2403.16707
| null | null |
http://arxiv.org/pdf/2403.16707v1
|
2024-03-25T12:44:52Z
|
2024-03-25T12:44:52Z
|
One-Shot Domain Incremental Learning
|
Domain incremental learning (DIL) has been discussed in previous studies on deep neural network models for classification. In DIL, we assume that samples on new domains are observed over time. The models must classify inputs on all domains. In practice, however, we may encounter a situation where we need to perform DIL under the constraint that the samples on the new domain are observed only infrequently. Therefore, in this study, we consider the extreme case where we have only one sample from the new domain, which we call one-shot DIL. We first empirically show that existing DIL methods do not work well in one-shot DIL. We have analyzed the reason for this failure through various investigations. According to our analysis, we clarify that the difficulty of one-shot DIL is caused by the statistics in the batch normalization layers. Therefore, we propose a technique regarding these statistics and demonstrate the effectiveness of our technique through experiments on open datasets.
|
[
"['Yasushi Esaki' 'Satoshi Koide' 'Takuro Kutsuna']"
] |
null | null |
2403.16768
| null | null |
http://arxiv.org/pdf/2403.16768v1
|
2024-03-25T13:46:09Z
|
2024-03-25T13:46:09Z
|
DeepKnowledge: Generalisation-Driven Deep Learning Testing
|
Despite their unprecedented success, DNNs are notoriously fragile to small shifts in data distribution, demanding effective testing techniques that can assess their dependability. Despite recent advances in DNN testing, there is a lack of systematic testing approaches that assess the DNN's capability to generalise and operate comparably beyond data in their training distribution. We address this gap with DeepKnowledge, a systematic testing methodology for DNN-based systems founded on the theory of knowledge generalisation, which aims to enhance DNN robustness and reduce the residual risk of 'black box' models. Conforming to this theory, DeepKnowledge posits that core computational DNN units, termed Transfer Knowledge neurons, can generalise under domain shift. DeepKnowledge provides an objective confidence measurement on testing activities of DNN given data distribution shifts and uses this information to instrument a generalisation-informed test adequacy criterion to check the transfer knowledge capacity of a test set. Our empirical evaluation of several DNNs, across multiple datasets and state-of-the-art adversarial generation techniques demonstrates the usefulness and effectiveness of DeepKnowledge and its ability to support the engineering of more dependable DNNs. We report improvements of up to 10 percentage points over state-of-the-art coverage criteria for detecting adversarial attacks on several benchmarks, including MNIST, SVHN, and CIFAR.
|
[
"['Sondess Missaoui' 'Simos Gerasimou' 'Nikolaos Matragkas']"
] |
null | null |
2403.16771
| null | null |
http://arxiv.org/pdf/2403.16771v2
|
2024-04-29T20:45:53Z
|
2024-03-25T13:50:11Z
|
Synthetic Data Generation and Joint Learning for Robust Code-Mixed
Translation
|
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity problem in low-resource setup is to leverage existing data in resource-rich language through translation. In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation. First, we synthetically develop HINMIX, a parallel corpus of Hinglish to English, with ~4.2M sentence pairs. Subsequently, we propose RCMT, a robust perturbation based joint-training model that learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. Further, we show the adaptability of RCMT in a zero-shot setup for Bengalish to English translation. Our evaluation and comprehensive analyses qualitatively and quantitatively demonstrate the superiority of RCMT over state-of-the-art code-mixed and robust translation methods.
|
[
"['Kartik Kartik' 'Sanjana Soni' 'Anoop Kunchukuttan' 'Tanmoy Chakraborty'\n 'Md Shad Akhtar']"
] |
null | null |
2403.16776
| null | null |
http://arxiv.org/pdf/2403.16776v1
|
2024-03-25T13:52:48Z
|
2024-03-25T13:52:48Z
|
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
|
Anatomical atlases are widely used for population analysis. Conditional atlases target a particular sub-population defined via certain conditions (e.g. demographics or pathologies) and allow for the investigation of fine-grained anatomical differences - such as morphological changes correlated with age. Existing approaches use either registration-based methods that are unable to handle large anatomical variations or generative models, which can suffer from training instabilities and hallucinations. To overcome these limitations, we use latent diffusion models to generate deformation fields, which transform a general population atlas into one representing a specific sub-population. By generating a deformation field and registering the conditional atlas to a neighbourhood of images, we ensure structural plausibility and avoid hallucinations, which can occur during direct image synthesis. We compare our method to several state-of-the-art atlas generation methods in experiments using 5000 brain as well as whole-body MR images from UK Biobank. Our method generates highly realistic atlases with smooth transformations and high anatomical fidelity, outperforming the baselines.
|
[
"['Sophie Starck' 'Vasiliki Sideri-Lampretsa' 'Bernhard Kainz'\n 'Martin Menten' 'Tamara Mueller' 'Daniel Rueckert']"
] |
null | null |
2403.16782
| null | null |
http://arxiv.org/pdf/2403.16782v1
|
2024-03-25T13:57:45Z
|
2024-03-25T13:57:45Z
|
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection
|
Adversarial attacks (AAs) pose a significant threat to the reliability and robustness of deep neural networks. While the impact of these attacks on model predictions has been extensively studied, their effect on the learned representations and concepts within these models remains largely unexplored. In this work, we perform an in-depth analysis of the influence of AAs on the concepts learned by convolutional neural networks (CNNs) using eXplainable artificial intelligence (XAI) techniques. Through an extensive set of experiments across various network architectures and targeted AA techniques, we unveil several key findings. First, AAs induce substantial alterations in the concept composition within the feature space, introducing new concepts or modifying existing ones. Second, the adversarial perturbation itself can be linearly decomposed into a set of latent vector components, with a subset of these being responsible for the attack's success. Notably, we discover that these components are target-specific, i.e., are similar for a given target class throughout different AA techniques and starting classes. Our findings provide valuable insights into the nature of AAs and their impact on learned representations, paving the way for the development of more robust and interpretable deep learning models, as well as effective defenses against adversarial threats.
|
[
"['Georgii Mikriukov' 'Gesina Schwalbe' 'Franz Motzkus' 'Korinna Bade']"
] |
null | null |
2403.16790
| null | null |
http://arxiv.org/pdf/2403.16790v1
|
2024-03-25T14:05:52Z
|
2024-03-25T14:05:52Z
|
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the
Isotropy of the Additive Gaussian Noise
|
Denoising Diffusion Probabilistic Models (DDPMs) have accomplished much in the realm of generative AI. Despite their high performance, there is room for improvement, especially in terms of sample fidelity by utilizing statistical properties that impose structural integrity, such as isotropy. Minimizing the mean squared error between the additive and predicted noise alone does not impose constraints on the predicted noise to be isotropic. Thus, we were motivated to utilize the isotropy of the additive noise as a constraint on the objective function to enhance the fidelity of DDPMs. Our approach is simple and can be applied to any DDPM variant. We validate our approach by presenting experiments conducted on four synthetic 2D datasets as well as on unconditional image generation. As demonstrated by the results, the incorporation of this constraint improves the fidelity metrics, Precision and Density for the 2D datasets as well as for the unconditional image generation.
|
[
"['Dilum Fernando' 'Dhananjaya jayasundara' 'Roshan Godaliyadda'\n 'Chaminda Bandara' 'Parakrama Ekanayake' 'Vijitha Herath']"
] |
null | null |
2403.16798
| null | null |
http://arxiv.org/pdf/2403.16798v2
|
2024-05-18T15:22:20Z
|
2024-03-25T14:17:38Z
|
Cluster-Based Normalization Layer for Neural Networks
|
Deep learning grapples with challenges in training neural networks, notably internal covariate shift and label shift. Conventional normalization techniques like Batch Normalization (BN) partially mitigate these issues but are hindered by constraints such as dependency on batch size and distribution assumptions. Similarly, mixture normalization (MN) encounters computational barriers in handling diverse Gaussian distributions. This paper introduces Cluster-based Normalization (CB-Norm), presenting two variants: Supervised Cluster-based Normalization (SCB-Norm) and Unsupervised Cluster-based Normalization (UCB-Norm), offering a pioneering single-step normalization strategy. CB-Norm employs a Gaussian mixture model to address gradient stability and learning acceleration challenges. SCB-Norm utilizes predefined data partitioning, termed clusters, for supervised normalization, while UCB-Norm adaptively clusters neuron activations during training, eliminating reliance on predefined partitions. This approach simultaneously tackles clustering and resolution tasks within neural networks, reducing computational complexity compared to existing methods. CB-Norm outperforms traditional techniques like BN and MN, enhancing neural network performance across diverse learning scenarios.
|
[
"['Bilal Faye' 'Hanane Azzag' 'Mustapha Lebbah']"
] |
null | null |
2403.16809
| null | null |
http://arxiv.org/pdf/2403.16809v1
|
2024-03-25T14:32:28Z
|
2024-03-25T14:32:28Z
|
An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems
|
The increasing prevalence of Cyber-Physical Systems and the Internet of Things (CPS-IoT) applications and Foundation Models are enabling new applications that leverage real-time control of the environment. For example, real-time control of Heating, Ventilation and Air-Conditioning (HVAC) systems can reduce its usage when not needed for the comfort of human occupants, hence reducing energy consumption. Collecting real-time feedback on human preferences in such human-in-the-loop (HITL) systems, however, is difficult in practice. We propose the use of large language models (LLMs) to deal with the challenges of dynamic environments and difficult-to-obtain data in CPS optimization. In this paper, we present a case study that employs LLM agents to mimic the behaviors and thermal preferences of various population groups (e.g. young families, the elderly) in a shopping mall. The aggregated thermal preferences are integrated into an agent-in-the-loop based reinforcement learning algorithm AitL-RL, which employs the LLM as a dynamic simulation of the physical environment to learn how to balance between energy savings and occupant comfort. Our results show that LLMs are capable of simulating complex population movements within large open spaces. Besides, AitL-RL demonstrates superior performance compared to the popular existing policy of set point control, suggesting that adaptive and personalized decision-making is critical for efficient optimization in CPS-IoT applications. Through this case study, we demonstrate the potential of integrating advanced Foundation Models like LLMs into CPS-IoT to enhance system adaptability and efficiency. The project's code can be found on our GitHub repository.
|
[
"['Hanqing Yang' 'Marie Siew' 'Carlee Joe-Wong']"
] |
null | null |
2403.16818
| null | null |
http://arxiv.org/pdf/2403.16818v1
|
2024-03-25T14:46:24Z
|
2024-03-25T14:46:24Z
|
Multiple-Source Localization from a Single-Snapshot Observation Using
Graph Bayesian Optimization
|
Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snapshot observation is especially relevant due to its prevalence. However, the inherent complexities of this problem, such as limited information, interactions among sources, and dependence on diffusion models, pose challenges to resolution. Current methods typically utilize heuristics and greedy selection, and they are usually bonded with one diffusion model. Consequently, their effectiveness is constrained. To address these limitations, we propose a simulation-based method termed BOSouL. Bayesian optimization (BO) is adopted to approximate the results for its sample efficiency. A surrogate function models uncertainty from the limited information. It takes sets of nodes as the input instead of individual nodes. BOSouL can incorporate any diffusion model in the data acquisition process through simulations. Empirical studies demonstrate that its performance is robust across graph structures and diffusion models. The code is available at https://github.com/XGraph-Team/BOSouL.
|
[
"['Zonghan Zhang' 'Zijian Zhang' 'Zhiqian Chen']"
] |
null | null |
2403.16823
| null | null |
http://arxiv.org/pdf/2403.16823v1
|
2024-03-25T14:48:00Z
|
2024-03-25T14:48:00Z
|
Resource and Mobility Management in Hybrid LiFi and WiFi Networks: A
User-Centric Learning Approach
|
Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) are an emerging indoor wireless communication paradigm, which combines the advantages of the capacious optical spectra of LiFi and ubiquitous coverage of WiFi. Meanwhile, load balancing (LB) becomes a key challenge in resource management for such hybrid networks. The existing LB methods are mostly network-centric, relying on a central unit to make a solution for the users all at once. Consequently, the solution needs to be updated for all users at the same pace, regardless of their moving status. This would affect the network performance in two aspects: i) when the update frequency is low, it would compromise the connectivity of fast-moving users; ii) when the update frequency is high, it would cause unnecessary handovers as well as hefty feedback costs for slow-moving users. Motivated by this, we investigate user-centric LB which allows users to update their solutions at different paces. The research is developed upon our previous work on adaptive target-condition neural network (ATCNN), which can conduct LB for individual users in quasi-static channels. In this paper, a deep neural network (DNN) model is designed to enable an adaptive update interval for each individual user. This new model is termed as mobility-supporting neural network (MSNN). Associating MSNN with ATCNN, a user-centric LB framework named mobility-supporting ATCNN (MS-ATCNN) is proposed to handle resource management and mobility management simultaneously. Results show that at the same level of average update interval, MS-ATCNN can achieve a network throughput up to 215% higher than conventional LB methods such as game theory, especially for a larger number of users. In addition, MS-ATCNN costs an ultra low runtime at the level of 100s $mu$s, which is two to three orders of magnitude lower than game theory.
|
[
"['Han Ji' 'Xiping Wu']"
] |
null | null |
2403.16825
| null | null |
http://arxiv.org/pdf/2403.16825v1
|
2024-03-25T14:49:01Z
|
2024-03-25T14:49:01Z
|
Weak Convergence Analysis of Online Neural Actor-Critic Algorithms
|
We prove that a single-layer neural network trained with the online actor critic algorithm converges in distribution to a random ordinary differential equation (ODE) as the number of hidden units and the number of training steps $rightarrow infty$. In the online actor-critic algorithm, the distribution of the data samples dynamically changes as the model is updated, which is a key challenge for any convergence analysis. We establish the geometric ergodicity of the data samples under a fixed actor policy. Then, using a Poisson equation, we prove that the fluctuations of the model updates around the limit distribution due to the randomly-arriving data samples vanish as the number of parameter updates $rightarrow infty$. Using the Poisson equation and weak convergence techniques, we prove that the actor neural network and critic neural network converge to the solutions of a system of ODEs with random initial conditions. Analysis of the limit ODE shows that the limit critic network will converge to the true value function, which will provide the actor an asymptotically unbiased estimate of the policy gradient. We then prove that the limit actor network will converge to a stationary point.
|
[
"['Samuel Chun-Hei Lam' 'Justin Sirignano' 'Ziheng Wang']"
] |
null | null |
2403.16829
| null | null |
http://arxiv.org/pdf/2403.16829v2
|
2024-04-23T13:54:27Z
|
2024-03-25T14:54:42Z
|
Convergence of a model-free entropy-regularized inverse reinforcement
learning algorithm
|
Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve entropy-regularized IRL problem. In particular, we employ a stochastic gradient descent update for the reward and a stochastic soft policy iteration update for the policy. Assuming access to a generative model, we prove that our algorithm is guaranteed to recover a reward for which the expert is $varepsilon$-optimal using $mathcal{O}(1/varepsilon^{2})$ samples of the Markov decision process (MDP). Furthermore, with $mathcal{O}(1/varepsilon^{4})$ samples we prove that the optimal policy corresponding to the recovered reward is $varepsilon$-close to the expert policy in total variation distance.
|
[
"['Titouan Renard' 'Andreas Schlaginhaufen' 'Tingting Ni'\n 'Maryam Kamgarpour']"
] |
null | null |
2403.16843
| null | null |
http://arxiv.org/pdf/2403.16843v2
|
2024-05-26T22:32:25Z
|
2024-03-25T15:04:11Z
|
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
|
Large language models (LLMs) have been increasingly employed for (interactive) decision-making, via the development of LLM-based autonomous agents. Despite their emerging successes, the performance of LLM agents in decision-making has not been fully investigated through quantitative metrics, especially in the multi-agent setting when they interact with each other, a typical scenario in real-world LLM-agent applications. To better understand the limits of LLM agents in these interactive environments, we propose to study their interactions in benchmark decision-making settings in online learning and game theory, through the performance metric of emph{regret}. We first empirically study the {no-regret} behaviors of LLMs in canonical (non-stationary) online learning problems, as well as the emergence of equilibria when LLM agents interact through playing repeated games. We then provide some theoretical insights into the no-regret behaviors of LLM agents, under certain assumptions on the supervised pre-training and the rationality model of human decision-makers who generate the data. Notably, we also identify (simple) cases where advanced LLMs such as GPT-4 fail to be no-regret. To promote the no-regret behaviors, we propose a novel emph{unsupervised} training loss of emph{regret-loss}, which, in contrast to the supervised pre-training loss, does not require the labels of (optimal) actions. We then establish the statistical guarantee of generalization bound for regret-loss minimization, followed by the optimization guarantee that minimizing such a loss may automatically lead to known no-regret learning algorithms. Our further experiments demonstrate the effectiveness of our regret-loss, especially in addressing the above ``regrettable'' cases.
|
[
"['Chanwoo Park' 'Xiangyu Liu' 'Asuman Ozdaglar' 'Kaiqing Zhang']"
] |
null | null |
2403.16846
| null | null |
http://arxiv.org/pdf/2403.16846v1
|
2024-03-25T15:07:50Z
|
2024-03-25T15:07:50Z
|
GreeDy and CoDy: Counterfactual Explainers for Dynamic Graphs
|
Temporal Graph Neural Networks (TGNNs), crucial for modeling dynamic graphs with time-varying interactions, face a significant challenge in explainability due to their complex model structure. Counterfactual explanations, crucial for understanding model decisions, examine how input graph changes affect outcomes. This paper introduces two novel counterfactual explanation methods for TGNNs: GreeDy (Greedy Explainer for Dynamic Graphs) and CoDy (Counterfactual Explainer for Dynamic Graphs). They treat explanations as a search problem, seeking input graph alterations that alter model predictions. GreeDy uses a simple, greedy approach, while CoDy employs a sophisticated Monte Carlo Tree Search algorithm. Experiments show both methods effectively generate clear explanations. Notably, CoDy outperforms GreeDy and existing factual methods, with up to 59% higher success rate in finding significant counterfactual inputs. This highlights CoDy's potential in clarifying TGNN decision-making, increasing their transparency and trustworthiness in practice.
|
[
"['Zhan Qu' 'Daniel Gomm' 'Michael Färber']"
] |
null | null |
2403.16851
| null | null |
http://arxiv.org/pdf/2403.16851v1
|
2024-03-25T15:15:09Z
|
2024-03-25T15:15:09Z
|
Can ChatGPT predict article retraction based on Twitter mentions?
|
Detecting problematic research articles timely is a vital task. This study explores whether Twitter mentions of retracted articles can signal potential problems with the articles prior to retraction, thereby playing a role in predicting future retraction of problematic articles. A dataset comprising 3,505 retracted articles and their associated Twitter mentions is analyzed, alongside 3,505 non-retracted articles with similar characteristics obtained using the Coarsened Exact Matching method. The effectiveness of Twitter mentions in predicting article retraction is evaluated by four prediction methods, including manual labelling, keyword identification, machine learning models, and ChatGPT. Manual labelling results indicate that there are indeed retracted articles with their Twitter mentions containing recognizable evidence signaling problems before retraction, although they represent only a limited share of all retracted articles with Twitter mention data (approximately 16%). Using the manual labelling results as the baseline, ChatGPT demonstrates superior performance compared to other methods, implying its potential in assisting human judgment for predicting article retraction. This study uncovers both the potential and limitation of social media events as an early warning system for article retraction, shedding light on a potential application of generative artificial intelligence in promoting research integrity.
|
[
"['Er-Te Zheng' 'Hui-Zhen Fu' 'Zhichao Fang']"
] |
null | null |
2403.16855
| null | null |
http://arxiv.org/pdf/2403.16855v1
|
2024-03-25T15:18:23Z
|
2024-03-25T15:18:23Z
|
Semantic-Aware Remote Estimation of Multiple Markov Sources Under
Constraints
|
This paper studies semantic-aware communication for remote estimation of multiple Markov sources over a lossy and rate-constrained channel. Unlike most existing studies that treat all source states equally, we exploit the semantics of information and consider that the remote actuator has different tolerances for the estimation errors of different states. We aim to find an optimal scheduling policy that minimizes the long-term state-dependent costs of estimation errors under a transmission frequency constraint. We theoretically show the structure of the optimal policy by leveraging the average-cost Constrained Markov Decision Process (CMDP) theory and the Lagrangian dynamic programming. By exploiting the optimal structural results, we develop a novel policy search algorithm, termed intersection search plus relative value iteration (Insec-RVI), that can find the optimal policy using only a few iterations. To avoid the ``curse of dimensionality'' of MDPs, we propose an online low-complexity drift-plus-penalty (DPP) scheduling algorithm based on the Lyapunov optimization theorem. We also design an efficient average-cost Q-learning algorithm to estimate the optimal policy without knowing a priori the channel and source statistics. Numerical results show that continuous transmission is inefficient, and remarkably, our semantic-aware policies can attain the optimum by strategically utilizing fewer transmissions by exploiting the timing of the important information.
|
[
"['Jiping Luo' 'Nikolaos Pappas']"
] |
null | null |
2403.16861
| null | null |
http://arxiv.org/pdf/2403.16861v2
|
2024-03-26T07:56:21Z
|
2024-03-25T15:26:10Z
|
DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts
|
The DISL dataset features a collection of $514,506$ unique Solidity files that have been deployed to Ethereum mainnet. It caters to the need for a large and diverse dataset of real-world smart contracts. DISL serves as a resource for developing machine learning systems and for benchmarking software engineering tools designed for smart contracts. By aggregating every verified smart contract from Etherscan up to January 15, 2024, DISL surpasses existing datasets in size and recency.
|
[
"['Gabriele Morello' 'Mojtaba Eshghie' 'Sofia Bobadilla' 'Martin Monperrus']"
] |
null | null |
2403.16862
| null | null |
http://arxiv.org/pdf/2403.16862v1
|
2024-03-25T15:26:32Z
|
2024-03-25T15:26:32Z
|
INPC: Implicit Neural Point Clouds for Radiance Field Rendering
|
We introduce a new approach for reconstruction and novel-view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene representation, which implicitly encodes a point cloud in a continuous octree-based probability field and a multi-resolution hash grid. In doing so, we combine the benefits of both worlds by retaining favorable behavior during optimization: Our novel implicit point cloud representation and differentiable bilinear rasterizer enable fast rendering while preserving fine geometric detail without depending on initial priors like structure-from-motion point clouds. Our method achieves state-of-the-art image quality on several common benchmark datasets. Furthermore, we achieve fast inference at interactive frame rates, and can extract explicit point clouds to further enhance performance.
|
[
"['Florian Hahlbohm' 'Linus Franke' 'Moritz Kappel' 'Susana Castillo'\n 'Marc Stamminger' 'Marcus Magnor']"
] |
null | null |
2403.16871
| null | null |
http://arxiv.org/pdf/2403.16871v1
|
2024-03-25T15:37:43Z
|
2024-03-25T15:37:43Z
|
Conformal Off-Policy Prediction for Multi-Agent Systems
|
Off-Policy Prediction (OPP), i.e., predicting the outcomes of a target policy using only data collected under a nominal (behavioural) policy, is a paramount problem in data-driven analysis of safety-critical systems where the deployment of a new policy may be unsafe. To achieve dependable off-policy predictions, recent work on Conformal Off-Policy Prediction (COPP) leverage the conformal prediction framework to derive prediction regions with probabilistic guarantees under the target process. Existing COPP methods can account for the distribution shifts induced by policy switching, but are limited to single-agent systems and scalar outcomes (e.g., rewards). In this work, we introduce MA-COPP, the first conformal prediction method to solve OPP problems involving multi-agent systems, deriving joint prediction regions for all agents' trajectories when one or more "ego" agents change their policies. Unlike the single-agent scenario, this setting introduces higher complexity as the distribution shifts affect predictions for all agents, not just the ego agents, and the prediction task involves full multi-dimensional trajectories, not just reward values. A key contribution of MA-COPP is to avoid enumeration or exhaustive search of the output space of agent trajectories, which is instead required by existing COPP methods to construct the prediction region. We achieve this by showing that an over-approximation of the true JPR can be constructed, without enumeration, from the maximum density ratio of the JPR trajectories. We evaluate the effectiveness of MA-COPP in multi-agent systems from the PettingZoo library and the F1TENTH autonomous racing environment, achieving nominal coverage in higher dimensions and various shift settings.
|
[
"['Tom Kuipers' 'Renukanandan Tumu' 'Shuo Yang' 'Milad Kazemi'\n 'Rahul Mangharam' 'Nicola Paoletti']"
] |
null | null |
2403.16877
| null | null |
http://arxiv.org/pdf/2403.16877v1
|
2024-03-25T15:42:09Z
|
2024-03-25T15:42:09Z
|
Proprioception Is All You Need: Terrain Classification for Boreal
Forests
|
Recent works in field robotics highlighted the importance of resiliency against different types of terrains. Boreal forests, in particular, are home to many mobility-impeding terrains that should be considered for off-road autonomous navigation. Also, being one of the largest land biomes on Earth, boreal forests are an area where autonomous vehicles are expected to become increasingly common. In this paper, we address this issue by introducing BorealTC, a publicly available dataset for proprioceptive-based terrain classification (TC). Recorded with a Husky A200, our dataset contains 116 min of Inertial Measurement Unit (IMU), motor current, and wheel odometry data, focusing on typical boreal forest terrains, notably snow, ice, and silty loam. Combining our dataset with another dataset from the state-of-the-art, we evaluate both a Convolutional Neural Network (CNN) and the novel state space model (SSM)-based Mamba architecture on a TC task. Interestingly, we show that while CNN outperforms Mamba on each separate dataset, Mamba achieves greater accuracy when trained on a combination of both. In addition, we demonstrate that Mamba's learning capacity is greater than a CNN for increasing amounts of data. We show that the combination of two TC datasets yields a latent space that can be interpreted with the properties of the terrains. We also discuss the implications of merging datasets on classification. Our source code and dataset are publicly available online: https://github.com/norlab-ulaval/BorealTC.
|
[
"['Damien LaRocque' 'William Guimont-Martin' 'David-Alexandre Duclos'\n 'Philippe Giguère' 'François Pomerleau']"
] |
null | null |
2403.16883
| null | null |
http://arxiv.org/pdf/2403.16883v2
|
2024-06-25T16:01:57Z
|
2024-03-25T15:53:32Z
|
GLAD: Improving Latent Graph Generative Modeling with Simple
Quantization
|
Exploring the graph latent structures has not garnered much attention in the graph generative research field. Yet, exploiting the latent space is as crucial as working on the data space for discrete data such as graphs. However, previous methods either failed to preserve the permutation symmetry of graphs or lacked an effective approaches to model appropriately within the latent space. To mitigate those issues, we propose a simple, yet effective discrete latent graph diffusion generative model. Our model, namely GLAD, not only overcomes the drawbacks of existing latent approaches, but also alleviates inherent issues present in diffusion methods applied on the graph space. We validate our generative model on the molecular benchmark datasets, on which it demonstrates competitive performance compared with the state-of-the-art baselines.
|
[
"['Van Khoa Nguyen' 'Yoann Boget' 'Frantzeska Lavda' 'Alexandros Kalousis']"
] |
null | null |
2403.16899
| null | null |
http://arxiv.org/pdf/2403.16899v1
|
2024-03-25T16:10:47Z
|
2024-03-25T16:10:47Z
|
State Space Models as Foundation Models: A Control Theoretic Overview
|
In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models. This is exemplified by the recent success of Mamba, showing better performance than the state-of-the-art Transformer architectures in language tasks. Foundation models, like e.g. GPT-4, aim to encode sequential data into a latent space in order to learn a compressed representation of the data. The same goal has been pursued by control theorists using SSMs to efficiently model dynamical systems. Therefore, SSMs can be naturally connected to deep sequence modeling, offering the opportunity to create synergies between the corresponding research areas. This paper is intended as a gentle introduction to SSM-based architectures for control theorists and summarizes the latest research developments. It provides a systematic review of the most successful SSM proposals and highlights their main features from a control theoretic perspective. Additionally, we present a comparative analysis of these models, evaluating their performance on a standardized benchmark designed for assessing a model's efficiency at learning long sequences.
|
[
"['Carmen Amo Alonso' 'Jerome Sieber' 'Melanie N. Zeilinger']"
] |
null | null |
2403.16915
| null | null |
http://arxiv.org/pdf/2403.16915v3
|
2024-03-27T01:53:36Z
|
2024-03-25T16:32:50Z
|
Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language
Models
|
Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations.
|
[
"['Atsushi Keyaki' 'Ribeka Keyaki']"
] |
null | null |
2403.16916
| null | null |
http://arxiv.org/pdf/2403.16916v1
|
2024-03-25T16:36:13Z
|
2024-03-25T16:36:13Z
|
SCOD: From Heuristics to Theory
|
This paper addresses the problem of designing reliable prediction models that abstain from predictions when faced with uncertain or out-of-distribution samples - a recently proposed problem known as Selective Classification in the presence of Out-of-Distribution data (SCOD). We make three key contributions to SCOD. Firstly, we demonstrate that the optimal SCOD strategy involves a Bayes classifier for in-distribution (ID) data and a selector represented as a stochastic linear classifier in a 2D space, using i) the conditional risk of the ID classifier, and ii) the likelihood ratio of ID and out-of-distribution (OOD) data as input. This contrasts with suboptimal strategies from current OOD detection methods and the Softmax Information Retaining Combination (SIRC), specifically developed for SCOD. Secondly, we establish that in a distribution-free setting, the SCOD problem is not Probably Approximately Correct learnable when relying solely on an ID data sample. Third, we introduce POSCOD, a simple method for learning a plugin estimate of the optimal SCOD strategy from both an ID data sample and an unlabeled mixture of ID and OOD data. Our empirical results confirm the theoretical findings and demonstrate that our proposed method, POSCOD, out performs existing OOD methods in effectively addressing the SCOD problem.
|
[
"['Vojtech Franc' 'Jakub Paplham' 'Daniel Prusa']"
] |
null | null |
2403.16930
| null | null |
http://arxiv.org/pdf/2403.16930v2
|
2024-04-02T13:33:06Z
|
2024-03-25T16:49:38Z
|
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
|
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It enables Artificial Intelligence (AI) at the edge by creating models without sharing actual data across the network. Existing research typically focuses on generic aspects of non-IID data and heterogeneity in client's system characteristics, but they often neglect the issue of insufficient data for model development, which can arise from uneven class label distribution and highly variable data volumes across edge nodes. In this work, we propose FLIGAN, a novel approach to address the issue of data incompleteness in FL. First, we leverage Generative Adversarial Networks (GANs) to adeptly capture complex data distributions and generate synthetic data that closely resemble real-world data. Then, we use synthetic data to enhance the robustness and completeness of datasets across nodes. Our methodology adheres to FL's privacy requirements by generating synthetic data in a federated manner without sharing the actual data in the process. We incorporate techniques such as classwise sampling and node grouping, designed to improve the federated GAN's performance, enabling the creation of high-quality synthetic datasets and facilitating efficient FL training. Empirical results from our experiments demonstrate that FLIGAN significantly improves model accuracy, especially in scenarios with high class imbalances, achieving up to a 20% increase in model accuracy over traditional FL baselines.
|
[
"['Paul Joe Maliakel' 'Shashikant Ilager' 'Ivona Brandic']"
] |
null | null |
2403.16933
| null | null |
http://arxiv.org/pdf/2403.16933v1
|
2024-03-25T16:57:02Z
|
2024-03-25T16:57:02Z
|
Backpropagation through space, time, and the brain
|
Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems -- whether biological or artificial -- are constrained by spatio-temporal locality. How such networks can perform efficient credit assignment, remains, to a large extent, an open question. In Machine Learning, the answer is almost universally given by the error backpropagation algorithm, through both space (BP) and time (BPTT). However, BP(TT) is well-known to rely on biologically implausible assumptions, in particular with respect to spatiotemporal (non-)locality, while forward-propagation models such as real-time recurrent learning (RTRL) suffer from prohibitive memory constraints. We introduce Generalized Latent Equilibrium (GLE), a computational framework for fully local spatio-temporal credit assignment in physical, dynamical networks of neurons. We start by defining an energy based on neuron-local mismatches, from which we derive both neuronal dynamics via stationarity and parameter dynamics via gradient descent. The resulting dynamics can be interpreted as a real-time, biologically plausible approximation of BPTT in deep cortical networks with continuous-time neuronal dynamics and continuously active, local synaptic plasticity. In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation. For the forward computation, it enables the mapping of time-continuous inputs to neuronal space, performing an effective spatiotemporal convolution. For the backward computation, it permits the temporal inversion of feedback signals, which consequently approximate the adjoint states necessary for useful parameter updates.
|
[
"['Benjamin Ellenberger' 'Paul Haider' 'Jakob Jordan' 'Kevin Max'\n 'Ismael Jaras' 'Laura Kriener' 'Federico Benitez' 'Mihai A. Petrovici']"
] |
null | null |
2403.16950
| null | null |
http://arxiv.org/pdf/2403.16950v2
|
2024-03-26T02:28:42Z
|
2024-03-25T17:11:28Z
|
Aligning with Human Judgement: The Role of Pairwise Preference in Large
Language Model Evaluators
|
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language. However, LLMs still exhibit biases in evaluation and often struggle to generate coherent evaluations that align with human assessments. In this work, we first conduct a systematic study of the misalignment between LLM evaluators and human judgement, revealing that existing calibration methods aimed at mitigating biases are insufficient for effectively aligning LLM evaluators. Inspired by the use of preference data in RLHF, we formulate the evaluation as a ranking problem and introduce Pairwise-preference Search (PairS), an uncertainty-guided search method that employs LLMs to conduct pairwise comparisons and efficiently ranks candidate texts. PairS achieves state-of-the-art performance on representative evaluation tasks and demonstrates significant improvements over direct scoring. Furthermore, we provide insights into the role of pairwise preference in quantifying the transitivity of LLMs and demonstrate how PairS benefits from calibration.
|
[
"['Yinhong Liu' 'Han Zhou' 'Zhijiang Guo' 'Ehsan Shareghi' 'Ivan Vulić'\n 'Anna Korhonen' 'Nigel Collier']"
] |
null | null |
2403.16952
| null | null |
http://arxiv.org/pdf/2403.16952v1
|
2024-03-25T17:14:00Z
|
2024-03-25T17:14:00Z
|
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language
Modeling Performance
|
Pretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models. While existing endeavors rely on heuristics or qualitative strategies to tune the proportions, we discover the quantitative predictability of model performance regarding the mixture proportions in function forms, which we refer to as the data mixing laws. Fitting such functions on sample mixtures unveils model performance on unseen mixtures before actual runs, thus guiding the selection of an ideal data mixture. Furthermore, we propose nested use of the scaling laws of training steps, model sizes, and our data mixing law to enable predicting the performance of large models trained on massive data under various mixtures with only small-scale training. Moreover, experimental results verify that our method effectively optimizes the training mixture of a 1B model trained for 100B tokens in RedPajama, reaching a performance comparable to the one trained for 48% more steps on the default mixture. Extending the application of data mixing laws to continual training accurately predicts the critical mixture proportion that avoids catastrophic forgetting and outlooks the potential for dynamic data schedules
|
[
"['Jiasheng Ye' 'Peiju Liu' 'Tianxiang Sun' 'Yunhua Zhou' 'Jun Zhan'\n 'Xipeng Qiu']"
] |
null | null |
2403.16967
| null | null |
http://arxiv.org/pdf/2403.16967v4
|
2024-05-14T05:12:27Z
|
2024-03-25T17:26:08Z
|
Visual Whole-Body Control for Legged Loco-Manipulation
|
We study the problem of mobile manipulation using legged robots equipped with an arm, namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an opportunity to amplify the manipulation capabilities by conducting whole-body control. That is, the robot can control the legs and the arm at the same time to extend its workspace. We propose a framework that can conduct the whole-body control autonomously with visual observations. Our approach, namely Visual Whole-Body Control(VBC), is composed of a low-level policy using all degrees of freedom to track the body velocities along with the end-effector position, and a high-level policy proposing the velocities and end-effector position based on visual inputs. We train both levels of policies in simulation and perform Sim2Real transfer for real robot deployment. We perform extensive experiments and show significant improvements over baselines in picking up diverse objects in different configurations (heights, locations, orientations) and environments.
|
[
"['Minghuan Liu' 'Zixuan Chen' 'Xuxin Cheng' 'Yandong Ji' 'Ri-Zhao Qiu'\n 'Ruihan Yang' 'Xiaolong Wang']"
] |
null | null |
2403.16970
| null | null |
http://arxiv.org/pdf/2403.16970v2
|
2024-03-29T16:14:41Z
|
2024-03-25T17:31:12Z
|
Joint chest X-ray diagnosis and clinical visual attention prediction
with multi-stage cooperative learning: enhancing interpretability
|
As deep learning has become the state-of-the-art for computer-assisted diagnosis, interpretability of the automatic decisions is crucial for clinical deployment. While various methods were proposed in this domain, visual attention maps of clinicians during radiological screening offer a unique asset to provide important insights and can potentially enhance the quality of computer-assisted diagnosis. With this paper, we introduce a novel deep-learning framework for joint disease diagnosis and prediction of corresponding visual saliency maps for chest X-ray scans. Specifically, we designed a novel dual-encoder multi-task UNet, which leverages both a DenseNet201 backbone and a Residual and Squeeze-and-Excitation block-based encoder to extract diverse features for saliency map prediction, and a multi-scale feature-fusion classifier to perform disease classification. To tackle the issue of asynchronous training schedules of individual tasks in multi-task learning, we proposed a multi-stage cooperative learning strategy, with contrastive learning for feature encoder pretraining to boost performance. Experiments show that our proposed method outperformed existing techniques for chest X-ray diagnosis and the quality of visual saliency map prediction.
|
[
"['Zirui Qiu' 'Hassan Rivaz' 'Yiming Xiao']"
] |
null | null |
2403.16973
| null | null |
http://arxiv.org/pdf/2403.16973v3
|
2024-06-14T00:29:46Z
|
2024-03-25T17:38:32Z
|
VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
|
We introduce VoiceCraft, a token infilling neural codec language model, that achieves state-of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on audiobooks, internet videos, and podcasts. VoiceCraft employs a Transformer decoder architecture and introduces a token rearrangement procedure that combines causal masking and delayed stacking to enable generation within an existing sequence. On speech editing tasks, VoiceCraft produces edited speech that is nearly indistinguishable from unedited recordings in terms of naturalness, as evaluated by humans; for zero-shot TTS, our model outperforms prior SotA models including VALLE and the popular commercial model XTTS-v2. Crucially, the models are evaluated on challenging and realistic datasets, that consist of diverse accents, speaking styles, recording conditions, and background noise and music, and our model performs consistently well compared to other models and real recordings. In particular, for speech editing evaluation, we introduce a high quality, challenging, and realistic dataset named RealEdit. We encourage readers to listen to the demos at https://jasonppy.github.io/VoiceCraft_web.
|
[
"['Puyuan Peng' 'Po-Yao Huang' 'Shang-Wen Li' 'Abdelrahman Mohamed'\n 'David Harwath']"
] |
null | null |
2403.16974
| null | null |
http://arxiv.org/pdf/2403.16974v1
|
2024-03-25T17:40:32Z
|
2024-03-25T17:40:32Z
|
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution
Microscopy
|
The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization. However, this methodology requires lengthy imaging times, which limits the ability to view dynamic interactions of live cells on short time scales. Many techniques have been developed to reduce the number of frames needed for localization, from classic iterative optimization to deep neural networks. Particularly, deep algorithm unrolling utilizes both the structure of iterative sparse recovery algorithms and the performance gains of supervised deep learning. However, the robustness of this approach is highly dependant on having sufficient training data. In this paper we introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder that learns only from given measurements. Our proposed method exceeds the performance of its supervised counterparts, thus allowing for robust, dynamic imaging well below the diffraction limit without any labeled training samples. Furthermore, the suggested model-based autoencoder scheme can be utilized to enhance generalization in any sparse recovery framework, without the need for external training data.
|
[
"['Yair Ben Sahel' 'Yonina C. Eldar']"
] |
null | null |
2403.16986
| null | null |
http://arxiv.org/pdf/2403.16986v2
|
2024-06-30T10:03:09Z
|
2024-03-25T17:48:06Z
|
Dynamic Relative Representations for Goal-Oriented Semantic
Communications
|
In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatches that might jeopardize understanding. In latent space communication, this challenge manifests as misalignment within high-dimensional representations where deep neural networks encode data. This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment. We propose a dynamic optimization strategy that adapts relative representations, communication parameters, and computation resources for energy-efficient, low-latency, goal-oriented semantic communications. Numerical results demonstrate our methodology's effectiveness in mitigating mismatches among devices, while optimizing energy consumption, delay, and effectiveness.
|
[
"['Simone Fiorellino' 'Claudio Battiloro' 'Emilio Calvanese Strinati'\n 'Paolo Di Lorenzo']"
] |
null | null |
2403.16990
| null | null |
http://arxiv.org/pdf/2403.16990v1
|
2024-03-25T17:52:07Z
|
2024-03-25T17:52:07Z
|
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image
Generation
|
Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images. However, they often struggle to faithfully capture the intended semantics of complex input prompts that include multiple subjects. Recently, numerous layout-to-image extensions have been introduced to improve user control, aiming to localize subjects represented by specific tokens. Yet, these methods often produce semantically inaccurate images, especially when dealing with multiple semantically or visually similar subjects. In this work, we study and analyze the causes of these limitations. Our exploration reveals that the primary issue stems from inadvertent semantic leakage between subjects in the denoising process. This leakage is attributed to the diffusion model's attention layers, which tend to blend the visual features of different subjects. To address these issues, we introduce Bounded Attention, a training-free method for bounding the information flow in the sampling process. Bounded Attention prevents detrimental leakage among subjects and enables guiding the generation to promote each subject's individuality, even with complex multi-subject conditioning. Through extensive experimentation, we demonstrate that our method empowers the generation of multiple subjects that better align with given prompts and layouts.
|
[
"['Omer Dahary' 'Or Patashnik' 'Kfir Aberman' 'Daniel Cohen-Or']"
] |
null | null |
2403.16995
| null | null |
http://arxiv.org/pdf/2403.16995v1
|
2024-03-25T17:58:22Z
|
2024-03-25T17:58:22Z
|
Language Rectified Flow: Advancing Diffusion Language Generation with
Probabilistic Flows
|
Recent works have demonstrated success in controlling sentence attributes ($e.g.$, sentiment) and structure ($e.g.$, syntactic structure) based on the diffusion language model. A key component that drives theimpressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of starting from the noise and the learning steps has limited its implementation to many NLP real-world applications. This paper proposes Language Rectified Flow ({ours}). Our method is based on the reformulation of the standard probabilistic flow models. Language rectified flow learns (neural) ordinary differential equation models to transport between the source distribution and the target distribution, hence providing a unified and effective solution to generative modeling and domain transfer. From the source distribution, our language rectified flow yields fast simulation and effectively decreases the inference time. Experiments on three challenging fine-grained control tasks and multiple high-quality text editing show that our method consistently outperforms its baselines. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many NLP tasks.
|
[
"['Shujian Zhang' 'Lemeng Wu' 'Chengyue Gong' 'Xingchao Liu']"
] |
null | null |
2403.17010
| null | null |
http://arxiv.org/pdf/2403.17010v1
|
2024-03-25T17:59:59Z
|
2024-03-25T17:59:59Z
|
Calib3D: Calibrating Model Preferences for Reliable 3D Scene
Understanding
|
Safety-critical 3D scene understanding tasks necessitate not only accurate but also confident predictions from 3D perception models. This study introduces Calib3D, a pioneering effort to benchmark and scrutinize the reliability of 3D scene understanding models from an uncertainty estimation viewpoint. We comprehensively evaluate 28 state-of-the-art models across 10 diverse 3D datasets, uncovering insightful phenomena that cope with both the aleatoric and epistemic uncertainties in 3D scene understanding. We discover that despite achieving impressive levels of accuracy, existing models frequently fail to provide reliable uncertainty estimates -- a pitfall that critically undermines their applicability in safety-sensitive contexts. Through extensive analysis of key factors such as network capacity, LiDAR representations, rasterization resolutions, and 3D data augmentation techniques, we correlate these aspects directly with the model calibration efficacy. Furthermore, we introduce DeptS, a novel depth-aware scaling approach aimed at enhancing 3D model calibration. Extensive experiments across a wide range of configurations validate the superiority of our method. We hope this work could serve as a cornerstone for fostering reliable 3D scene understanding. Code and benchmark toolkits are publicly available.
|
[
"['Lingdong Kong' 'Xiang Xu' 'Jun Cen' 'Wenwei Zhang' 'Liang Pan'\n 'Kai Chen' 'Ziwei Liu']"
] |
null | null |
2403.17011
| null | null |
http://arxiv.org/pdf/2403.17011v1
|
2024-01-02T18:12:03Z
|
2024-01-02T18:12:03Z
|
SUDO: a framework for evaluating clinical artificial intelligence
systems without ground-truth annotations
|
A clinical artificial intelligence (AI) system is often validated on a held-out set of data which it has not been exposed to before (e.g., data from a different hospital with a distinct electronic health record system). This evaluation process is meant to mimic the deployment of an AI system on data in the wild; those which are currently unseen by the system yet are expected to be encountered in a clinical setting. However, when data in the wild differ from the held-out set of data, a phenomenon referred to as distribution shift, and lack ground-truth annotations, it becomes unclear the extent to which AI-based findings can be trusted on data in the wild. Here, we introduce SUDO, a framework for evaluating AI systems without ground-truth annotations. SUDO assigns temporary labels to data points in the wild and directly uses them to train distinct models, with the highest performing model indicative of the most likely label. Through experiments with AI systems developed for dermatology images, histopathology patches, and clinical reports, we show that SUDO can be a reliable proxy for model performance and thus identify unreliable predictions. We also demonstrate that SUDO informs the selection of models and allows for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. The ability to triage unreliable predictions for further inspection and assess the algorithmic bias of AI systems can improve the integrity of research findings and contribute to the deployment of ethical AI systems in medicine.
|
[
"['Dani Kiyasseh' 'Aaron Cohen' 'Chengsheng Jiang' 'Nicholas Altieri']"
] |
null | null |
2403.17013
| null | null |
http://arxiv.org/pdf/2403.17013v1
|
2024-02-12T16:24:13Z
|
2024-02-12T16:24:13Z
|
Temporal-Spatial Processing of Event Camera Data via Delay-Loop
Reservoir Neural Network
|
This paper describes a temporal-spatial model for video processing with special applications to processing event camera videos. We propose to study a conjecture motivated by our previous study of video processing with delay loop reservoir (DLR) neural network, which we call Temporal-Spatial Conjecture (TSC). The TSC postulates that there is significant information content carried in the temporal representation of a video signal and that machine learning algorithms would benefit from separate optimization of the spatial and temporal components for intelligent processing. To verify or refute the TSC, we propose a Visual Markov Model (VMM) which decompose the video into spatial and temporal components and estimate the mutual information (MI) of these components. Since computation of video mutual information is complex and time consuming, we use a Mutual Information Neural Network to estimate the bounds of the mutual information. Our result shows that the temporal component carries significant MI compared to that of the spatial component. This finding has often been overlooked in neural network literature. In this paper, we will exploit this new finding to guide our design of a delay-loop reservoir neural network for event camera classification, which results in a 18% improvement on classification accuracy.
|
[
"['Richard Lau' 'Anthony Tylan-Tyler' 'Lihan Yao' 'Rey de Castro Roberto'\n 'Robert Taylor' 'Isaiah Jones']"
] |
null | null |
2403.17014
| null | null |
http://arxiv.org/pdf/2403.17014v1
|
2024-02-12T21:33:46Z
|
2024-02-12T21:33:46Z
|
Contrastive Learning for Regression on Hyperspectral Data
|
Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models, achieving better scores than other state-of-the-art transformations.
|
[
"['Mohamad Dhaini' 'Maxime Berar' 'Paul Honeine' 'Antonin Van Exem']"
] |
null | null |
2403.17016
| null | null |
http://arxiv.org/pdf/2403.17016v1
|
2024-02-14T22:10:52Z
|
2024-02-14T22:10:52Z
|
HEAL-ViT: Vision Transformers on a spherical mesh for medium-range
weather forecasting
|
In recent years, a variety of ML architectures and techniques have seen success in producing skillful medium range weather forecasts. In particular, Vision Transformer (ViT)-based models (e.g. Pangu-Weather, FuXi) have shown strong performance, working nearly "out-of-the-box" by treating weather data as a multi-channel image on a rectilinear grid. While a rectilinear grid is appropriate for 2D images, weather data is inherently spherical and thus heavily distorted at the poles on a rectilinear grid, leading to disproportionate compute being used to model data near the poles. Graph-based methods (e.g. GraphCast) do not suffer from this problem, as they map the longitude-latitude grid to a spherical mesh, but are generally more memory intensive and tend to need more compute resources for training and inference. While spatially homogeneous, the spherical mesh does not lend itself readily to be modeled by ViT-based models that implicitly rely on the rectilinear grid structure. We present HEAL-ViT, a novel architecture that uses ViT models on a spherical mesh, thus benefiting from both the spatial homogeneity enjoyed by graph-based models and efficient attention-based mechanisms exploited by transformers. HEAL-ViT produces weather forecasts that outperform the ECMWF IFS on key metrics, and demonstrate better bias accumulation and blurring than other ML weather prediction models. Further, the lowered compute footprint of HEAL-ViT makes it attractive for operational use as well, where other models in addition to a 6-hourly prediction model may be needed to produce the full set of operational forecasts required.
|
[
"['Vivek Ramavajjala']"
] |
null | null |
2403.17031
| null | null |
http://arxiv.org/pdf/2403.17031v1
|
2024-03-24T02:59:27Z
|
2024-03-24T02:59:27Z
|
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR
Summarization
|
This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work. We create an RLHF pipeline from scratch, enumerate over 20 key implementation details, and share key insights during the reproduction. Our RLHF-trained Pythia models demonstrate significant gains in response quality that scale with model size, with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint. We publicly release the trained model checkpoints and code to facilitate further research and accelerate progress in the field (url{https://github.com/vwxyzjn/summarize_from_feedback_details}).
|
[
"['Shengyi Huang' 'Michael Noukhovitch' 'Arian Hosseini' 'Kashif Rasul'\n 'Weixun Wang' 'Lewis Tunstall']"
] |
null | null |
2403.17032
| null | null |
http://arxiv.org/pdf/2403.17032v1
|
2024-03-24T06:52:37Z
|
2024-03-24T06:52:37Z
|
Stochastic parameter reduced-order model based on hybrid machine
learning approaches
|
Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural phenomena makes it extremely challenging to develop full-order models (FOMs) and apply them to studying many quantities of interest. In contrast, appropriate reduced-order models (ROMs) are favored due to their high computational efficiency and ability to describe the key dynamics and statistical characteristics of natural phenomena. Taking the viscous Burgers equation as an example, this paper constructs a Convolutional Autoencoder-Reservoir Computing-Normalizing Flow algorithm framework, where the Convolutional Autoencoder is used to construct latent space representations, and the Reservoir Computing-Normalizing Flow framework is used to characterize the evolution of latent state variables. In this way, a data-driven stochastic parameter reduced-order model is constructed to describe the complex system and its dynamic behavior.
|
[
"['Cheng Fang' 'Jinqiao Duan']"
] |
null | null |
2403.17040
| null | null |
http://arxiv.org/pdf/2403.17040v1
|
2024-03-25T12:15:10Z
|
2024-03-25T12:15:10Z
|
Enhancing Graph Representation Learning with Attention-Driven Spiking
Neural Networks
|
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spiking neural networks (SNNs) have recently emerged as a promising alternative to traditional neural networks for graph learning tasks, benefiting from their ability to efficiently encode and process temporal and spatial information. In this paper, we propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning. Specifically, we introduce an attention mechanism for SNN that can selectively focus on important nodes and corresponding features in a graph during the learning process. We evaluate our proposed method on several benchmark datasets and show that it achieves comparable performance compared to existing graph learning techniques.
|
[
"['Huifeng Yin' 'Mingkun Xu' 'Jing Pei' 'Lei Deng']"
] |
null | null |
2403.17042
| null | null |
http://arxiv.org/pdf/2403.17042v2
|
2024-06-12T01:33:21Z
|
2024-03-25T15:58:26Z
|
Provably Robust Score-Based Diffusion Posterior Sampling for
Plug-and-Play Image Reconstruction
|
In a great number of tasks in science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain sensing or imaging modality. Due to resource constraints, this task is often extremely ill-posed, which necessitates the adoption of expressive prior information to regularize the solution space. Score-based diffusion models, due to its impressive empirical success, have emerged as an appealing candidate of an expressive prior in image reconstruction. In order to accommodate diverse tasks at once, it is of great interest to develop efficient, consistent and robust algorithms that incorporate unconditional score functions of an image prior distribution in conjunction with flexible choices of forward models. This work develops an algorithmic framework for employing score-based diffusion models as an expressive data prior in general nonlinear inverse problems. Motivated by the plug-and-play framework in the imaging community, we introduce a diffusion plug-and-play method (DPnP) that alternatively calls two samplers, a proximal consistency sampler based solely on the likelihood function of the forward model, and a denoising diffusion sampler based solely on the score functions of the image prior. The key insight is that denoising under white Gaussian noise can be solved rigorously via both stochastic (i.e., DDPM-type) and deterministic (i.e., DDIM-type) samplers using the unconditional score functions. We establish both asymptotic and non-asymptotic performance guarantees of DPnP, and provide numerical experiments to illustrate its promise in solving both linear and nonlinear image reconstruction tasks. To the best of our knowledge, DPnP is the first provably-robust posterior sampling method for nonlinear inverse problems using unconditional diffusion priors.
|
[
"['Xingyu Xu' 'Yuejie Chi']"
] |
null | null |
2403.17064
| null | null |
http://arxiv.org/pdf/2403.17064v1
|
2024-03-25T18:00:42Z
|
2024-03-25T18:00:42Z
|
Continuous, Subject-Specific Attribute Control in T2I Models by
Identifying Semantic Directions
|
In recent years, advances in text-to-image (T2I) diffusion models have substantially elevated the quality of their generated images. However, achieving fine-grained control over attributes remains a challenge due to the limitations of natural language prompts (such as no continuous set of intermediate descriptions existing between ``person'' and ``old person''). Even though many methods were introduced that augment the model or generation process to enable such control, methods that do not require a fixed reference image are limited to either enabling global fine-grained attribute expression control or coarse attribute expression control localized to specific subjects, not both simultaneously. We show that there exist directions in the commonly used token-level CLIP text embeddings that enable fine-grained subject-specific control of high-level attributes in text-to-image models. Based on this observation, we introduce one efficient optimization-free and one robust optimization-based method to identify these directions for specific attributes from contrastive text prompts. We demonstrate that these directions can be used to augment the prompt text input with fine-grained control over attributes of specific subjects in a compositional manner (control over multiple attributes of a single subject) without having to adapt the diffusion model. Project page: https://compvis.github.io/attribute-control. Code is available at https://github.com/CompVis/attribute-control.
|
[
"['Stefan Andreas Baumann' 'Felix Krause' 'Michael Neumayr' 'Nick Stracke'\n 'Vincent Tao Hu' 'Björn Ommer']"
] |
null | null |
2403.17081
| null | null |
http://arxiv.org/pdf/2403.17081v1
|
2024-03-25T18:12:16Z
|
2024-03-25T18:12:16Z
|
Machine Learning on Blockchain Data: A Systematic Mapping Study
|
Context: Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic of interest for Machine Learning (ML). Objective: The objective of this paper is to provide a comprehensive review of the state of the art on machine learning applied to blockchain data. This work aims to systematically identify, analyze, and classify the literature on ML applied to blockchain data. This will allow us to discover the fields where more effort should be placed in future research. Method: A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 159 articles were selected and classified according to various dimensions, specifically, the domain use case, the blockchain, the data, and the machine learning models. Results: The majority of the papers (49.7%) fall within the Anomaly use case. Bitcoin (47.2%) was the blockchain that drew the most attention. A dataset consisting of more than 1.000.000 data points was used by 31.4% of the papers. And Classification (46.5%) was the ML task most applied to blockchain data. Conclusion: The results confirm that ML applied to blockchain data is a relevant and a growing topic of interest both in the literature and in practice. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel machine learning algorithms, the lack of a standardization framework, blockchain scalability issues and cross-chain interactions as areas worth exploring in the future.
|
[
"['Georgios Palaiokrassas' 'Sarah Bouraga' 'Leandros Tassiulas']"
] |
null | null |
2403.17083
| null | null |
http://arxiv.org/pdf/2403.17083v2
|
2024-06-08T07:53:16Z
|
2024-03-25T18:16:34Z
|
A Study in Dataset Pruning for Image Super-Resolution
|
In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword. While offering rich training material, they also demand substantial computational and storage resources. In this work, we analyze dataset pruning to solve these challenges. We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model. By focusing the training on just 50% of the original dataset, specifically on the samples characterized by the highest loss values, we achieve results comparable to or surpassing those obtained from training on the entire dataset. Interestingly, our analysis reveals that the top 5% of samples with the highest loss values negatively affect the training process. Excluding these samples and adjusting the selection to favor easier samples further enhances training outcomes. Our work opens new perspectives to the untapped potential of dataset pruning in image SR. It suggests that careful selection of training data based on loss-value metrics can lead to better SR models, challenging the conventional wisdom that more data inevitably leads to better performance.
|
[
"['Brian B. Moser' 'Federico Raue' 'Andreas Dengel']"
] |
null | null |
2403.17091
| null | null |
http://arxiv.org/pdf/2403.17091v1
|
2024-03-25T18:28:45Z
|
2024-03-25T18:28:45Z
|
Offline Reinforcement Learning: Role of State Aggregation and Trajectory
Data
|
We revisit the problem of offline reinforcement learning with value function realizability but without Bellman completeness. Previous work by Xie and Jiang (2021) and Foster et al. (2022) left open the question whether a bounded concentrability coefficient along with trajectory-based offline data admits a polynomial sample complexity. In this work, we provide a negative answer to this question for the task of offline policy evaluation. In addition to addressing this question, we provide a rather complete picture for offline policy evaluation with only value function realizability. Our primary findings are threefold: 1) The sample complexity of offline policy evaluation is governed by the concentrability coefficient in an aggregated Markov Transition Model jointly determined by the function class and the offline data distribution, rather than that in the original MDP. This unifies and generalizes the ideas of Xie and Jiang (2021) and Foster et al. (2022), 2) The concentrability coefficient in the aggregated Markov Transition Model may grow exponentially with the horizon length, even when the concentrability coefficient in the original MDP is small and the offline data is admissible (i.e., the data distribution equals the occupancy measure of some policy), 3) Under value function realizability, there is a generic reduction that can convert any hard instance with admissible data to a hard instance with trajectory data, implying that trajectory data offers no extra benefits over admissible data. These three pieces jointly resolve the open problem, though each of them could be of independent interest.
|
[
"['Zeyu Jia' 'Alexander Rakhlin' 'Ayush Sekhari' 'Chen-Yu Wei']"
] |
null | null |
2403.17093
| null | null |
http://arxiv.org/pdf/2403.17093v1
|
2024-03-25T18:32:22Z
|
2024-03-25T18:32:22Z
|
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep
Learning and Explainable AI Analysis
|
In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model's transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.
|
[
"['Ekramul Haque' 'Kamrul Hasan' 'Imtiaz Ahmed' 'Md. Sahabul Alam'\n 'Tariqul Islam']"
] |
null | null |
2403.17094
| null | null |
http://arxiv.org/pdf/2403.17094v1
|
2024-03-25T18:32:41Z
|
2024-03-25T18:32:41Z
|
SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end
Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving
|
To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photo-realistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-world foggy images.
|
[
"['Yiming Xie' 'Henglu Wei' 'Zhenyi Liu' 'Xiaoyu Wang' 'Xiangyang Ji']"
] |
null | null |
2403.17105
| null | null |
http://arxiv.org/pdf/2403.17105v1
|
2024-03-25T18:43:58Z
|
2024-03-25T18:43:58Z
|
Stochastic Gradient Langevin Unlearning
|
``The right to be forgotten'' ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points on the trained model parameters so that it can be approximately the same as if one retrains the model from scratch. This work proposes stochastic gradient Langevin unlearning, the first unlearning framework based on noisy stochastic gradient descent (SGD) with privacy guarantees for approximate unlearning problems under convexity assumption. Our results show that mini-batch gradient updates provide a superior privacy-complexity trade-off compared to the full-batch counterpart. There are numerous algorithmic benefits of our unlearning approach, including complexity saving compared to retraining, and supporting sequential and batch unlearning. To examine the privacy-utility-complexity trade-off of our method, we conduct experiments on benchmark datasets compared against prior works. Our approach achieves a similar utility under the same privacy constraint while using $2%$ and $10%$ of the gradient computations compared with the state-of-the-art gradient-based approximate unlearning methods for mini-batch and full-batch settings, respectively.
|
[
"['Eli Chien' 'Haoyu Wang' 'Ziang Chen' 'Pan Li']"
] |
null | null |
2403.17124
| null | null |
http://arxiv.org/pdf/2403.17124v2
|
2024-04-29T04:34:52Z
|
2024-03-25T19:04:59Z
|
Grounding Language Plans in Demonstrations Through Counterfactual
Perturbations
|
Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI. Whereas prior works have focused on leveraging LLMs directly for planning in symbolic spaces, this work uses LLMs to guide the search of task structures and constraints implicit in multi-step demonstrations. Specifically, we borrow from manipulation planning literature the concept of mode families, which group robot configurations by specific motion constraints, to serve as an abstraction layer between the high-level language representations of an LLM and the low-level physical trajectories of a robot. By replaying a few human demonstrations with synthetic perturbations, we generate coverage over the demonstrations' state space with additional successful executions as well as counterfactuals that fail the task. Our explanation-based learning framework trains an end-to-end differentiable neural network to predict successful trajectories from failures and as a by-product learns classifiers that ground low-level states and images in mode families without dense labeling. The learned grounding classifiers can further be used to translate language plans into reactive policies in the physical domain in an interpretable manner. We show our approach improves the interpretability and reactivity of imitation learning through 2D navigation and simulated and real robot manipulation tasks. Website: https://yanweiw.github.io/glide
|
[
"['Yanwei Wang' 'Tsun-Hsuan Wang' 'Jiayuan Mao' 'Michael Hagenow'\n 'Julie Shah']"
] |
null | null |
2403.17130
| null | null |
http://arxiv.org/pdf/2403.17130v1
|
2024-03-25T19:15:19Z
|
2024-03-25T19:15:19Z
|
Exploring the potential of prototype-based soft-labels data distillation
for imbalanced data classification
|
Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset. Consequently, data distillation methods are usually tied to a specific ML algorithm. While recent literature deals mainly with distillation of large collections of images in the context of neural network models, tabular data distillation is much less represented and mainly focused on a theoretical perspective. The current paper explores the potential of a simple distillation technique previously proposed in the context of Less-than-one shot learning. The main goal is to push further the performance of prototype-based soft-labels distillation in terms of classification accuracy, by integrating optimization steps in the distillation process. The analysis is performed on real-world data sets with various degrees of imbalance. Experimental studies trace the capability of the method to distill the data, but also the opportunity to act as an augmentation method, i.e. to generate new data that is able to increase model accuracy when used in conjunction with - as opposed to instead of - the original data.
|
[
"['Radu-Andrei Rosu' 'Mihaela-Elena Breaban' 'Henri Luchian']"
] |
null | null |
2403.17135
| null | null |
http://arxiv.org/pdf/2403.17135v1
|
2024-03-25T19:17:59Z
|
2024-03-25T19:17:59Z
|
Exploring the Generalization of Cancer Clinical Trial Eligibility
Classifiers Across Diseases
|
Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment. This study aims to evaluate the generalizability of eligibility classification across a broad spectrum of clinical trials. Starting with phase 3 cancer trials, annotated with seven eligibility exclusions, then to determine how well models can generalize to non-cancer and non-phase 3 trials. To assess this, we have compiled eligibility criteria data for five types of trials: (1) additional phase 3 cancer trials, (2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes trials, and (5) observational trials for any disease, comprising 2,490 annotated eligibility criteria across seven exclusion types. Our results show that models trained on the extensive cancer dataset can effectively handle criteria commonly found in non-cancer trials, such as autoimmune diseases. However, they struggle with criteria disproportionately prevalent in cancer trials, like prior malignancy. We also experiment with few-shot learning, demonstrating that a limited number of disease-specific examples can partially overcome this performance gap. We are releasing this new dataset of annotated eligibility statements to promote the development of cross-disease generalization in clinical trial classification.
|
[
"['Yumeng Yang' 'Ashley Gilliam' 'Ethan B Ludmir' 'Kirk Roberts']"
] |
null | null |
2403.17142
| null | null |
http://arxiv.org/pdf/2403.17142v2
|
2024-04-16T20:55:17Z
|
2024-03-25T19:39:17Z
|
Approximation with Random Shallow ReLU Networks with Applications to
Model Reference Adaptive Control
|
Neural networks are regularly employed in adaptive control of nonlinear systems and related methods of reinforcement learning. A common architecture uses a neural network with a single hidden layer (i.e. a shallow network), in which the weights and biases are fixed in advance and only the output layer is trained. While classical results show that there exist neural networks of this type that can approximate arbitrary continuous functions over bounded regions, they are non-constructive, and the networks used in practice have no approximation guarantees. Thus, the approximation properties required for control with neural networks are assumed, rather than proved. In this paper, we aim to fill this gap by showing that for sufficiently smooth functions, ReLU networks with randomly generated weights and biases achieve $L_{infty}$ error of $O(m^{-1/2})$ with high probability, where $m$ is the number of neurons. It suffices to generate the weights uniformly over a sphere and the biases uniformly over an interval. We show how the result can be used to get approximations of required accuracy in a model reference adaptive control application.
|
[
"['Andrew Lamperski' 'Tyler Lekang']"
] |
null | null |
2403.17143
| null | null |
http://arxiv.org/pdf/2403.17143v2
|
2024-03-27T15:15:16Z
|
2024-03-25T19:40:26Z
|
Guided Distant Supervision for Multilingual Relation Extraction Data:
Adapting to a New Language
|
Relation extraction is essential for extracting and understanding biographical information in the context of digital humanities and related subjects. There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. However, annotating such datasets can be expensive and time-consuming, in addition to being limited to English. This paper applies guided distant supervision to create a large biographical relationship extraction dataset for German. Our dataset, composed of more than 80,000 instances for nine relationship types, is the largest biographical German relationship extraction dataset. We also create a manually annotated dataset with 2000 instances to evaluate the models and release it together with the dataset compiled using guided distant supervision. We train several state-of-the-art machine learning models on the automatically created dataset and release them as well. Furthermore, we experiment with multilingual and cross-lingual experiments that could benefit many low-resource languages.
|
[
"['Alistair Plum' 'Tharindu Ranasinghe' 'Christoph Purschke']"
] |
null | null |
2403.17159
| null | null |
http://arxiv.org/pdf/2403.17159v1
|
2024-03-25T20:16:16Z
|
2024-03-25T20:16:16Z
|
Less Is More -- On the Importance of Sparsification for Transformers and
Graph Neural Networks for TSP
|
Most of the recent studies tackling routing problems like the Traveling Salesman Problem (TSP) with machine learning use a transformer or Graph Neural Network (GNN) based encoder architecture. However, many of them apply these encoders naively by allowing them to aggregate information over the whole TSP instances. We, on the other hand, propose a data preprocessing method that allows the encoders to focus on the most relevant parts of the TSP instances only. In particular, we propose graph sparsification for TSP graph representations passed to GNNs and attention masking for TSP instances passed to transformers where the masks correspond to the adjacency matrices of the sparse TSP graph representations. Furthermore, we propose ensembles of different sparsification levels allowing models to focus on the most promising parts while also allowing information flow between all nodes of a TSP instance. In the experimental studies, we show that for GNNs appropriate sparsification and ensembles of different sparsification levels lead to substantial performance increases of the overall architecture. We also design a new, state-of-the-art transformer encoder with ensembles of attention masking. These transformers increase model performance from a gap of $0.16%$ to $0.10%$ for TSP instances of size 100 and from $0.02%$ to $0.00%$ for TSP instances of size 50.
|
[
"['Attila Lischka' 'Jiaming Wu' 'Rafael Basso' 'Morteza Haghir Chehreghani'\n 'Balázs Kulcsár']"
] |
null | null |
2403.17164
| null | null |
http://arxiv.org/abs/2403.17164v2
|
2024-06-21T09:26:34Z
|
2024-03-25T20:29:04Z
|
Multi-Objective Quality-Diversity for Crystal Structure Prediction
|
Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their atomic configurations. However, prevailing Crystal Structure Prediction methods focus on identifying the most stable solutions that lie at the global minimum of the energy function. This approach overlooks other potentially interesting materials that lie in neighbouring local minima and have different material properties such as conductivity or resistance to deformation. By contrast, Quality-Diversity algorithms provide a promising avenue for Crystal Structure Prediction as they aim to find a collection of high-performing solutions that have diverse characteristics. However, it may also be valuable to optimise for the stability of crystal structures alongside other objectives such as magnetism or thermoelectric efficiency. Therefore, in this work, we harness the power of Multi-Objective Quality-Diversity algorithms in order to find crystal structures which have diverse features and achieve different trade-offs of objectives. We analyse our approach on 5 crystal systems and demonstrate that it is not only able to re-discover known real-life structures, but also find promising new ones. Moreover, we propose a method for illuminating the objective space to gain an understanding of what trade-offs can be achieved.
|
[
"['Hannah Janmohamed' 'Marta Wolinska' 'Shikha Surana' 'Thomas Pierrot'\n 'Aron Walsh' 'Antoine Cully']"
] |
null | null |
2403.17174
| null | null |
http://arxiv.org/pdf/2403.17174v1
|
2024-03-25T20:43:17Z
|
2024-03-25T20:43:17Z
|
Belief Samples Are All You Need For Social Learning
|
In this paper, we consider the problem of social learning, where a group of agents embedded in a social network are interested in learning an underlying state of the world. Agents have incomplete, noisy, and heterogeneous sources of information, providing them with recurring private observations of the underlying state of the world. Agents can share their learning experience with their peers by taking actions observable to them, with values from a finite feasible set of states. Actions can be interpreted as samples from the beliefs which agents may form and update on what the true state of the world is. Sharing samples, in place of full beliefs, is motivated by the limited communication, cognitive, and information-processing resources available to agents especially in large populations. Previous work (Salhab et al.) poses the question as to whether learning with probability one is still achievable if agents are only allowed to communicate samples from their beliefs. We provide a definite positive answer to this question, assuming a strongly connected network and a ``collective distinguishability'' assumption, which are both required for learning even in full-belief-sharing settings. In our proposed belief update mechanism, each agent's belief is a normalized weighted geometric interpolation between a fully Bayesian private belief -- aggregating information from the private source -- and an ensemble of empirical distributions of the samples shared by her neighbors over time. By carefully constructing asymptotic almost-sure lower/upper bounds on the frequency of shared samples matching the true state/or not, we rigorously prove the convergence of all the beliefs to the true state, with probability one.
|
[
"['Mahyar JafariNodeh' 'Amir Ajorlou' 'Ali Jadbabaie']"
] |
null | null |
2403.17177
| null | null |
http://arxiv.org/pdf/2403.17177v1
|
2024-03-25T20:44:01Z
|
2024-03-25T20:44:01Z
|
Brain Stroke Segmentation Using Deep Learning Models: A Comparative
Study
|
Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. Segmenting stroke lesions accurately is a challenging task, given that conventional manual techniques are time consuming and prone to errors. Recently, advanced deep models have been introduced for general medical image segmentation, demonstrating promising results that surpass many state of the art networks when evaluated on specific datasets. With the advent of the vision Transformers, several models have been introduced based on them, while others have aimed to design better modules based on traditional convolutional layers to extract long-range dependencies like Transformers. The question of whether such high-level designs are necessary for all segmentation cases to achieve the best results remains unanswered. In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well-known self-adaptive nnUNet framework with its configuration based on given data. We examined their performance on two publicly available datasets, and found that the nnUNet achieved the best results with the simplest design among all. Revealing the robustness issue of Transformers to such variabilities serves as a potential reason for their weaker performance. Furthermore, nnUNet's success underscores the significant impact of preprocessing and postprocessing techniques in enhancing segmentation results, surpassing the focus solely on architectural designs
|
[
"['Ahmed Soliman' 'Yousif Yousif' 'Ahmed Ibrahim' 'Yalda Zafari-Ghadim'\n 'Essam A. Rashed' 'Mohamed Mabrok']"
] |
null | null |
2403.17181
| null | null |
http://arxiv.org/pdf/2403.17181v1
|
2024-03-25T20:47:10Z
|
2024-03-25T20:47:10Z
|
On the Intersection of Signal Processing and Machine Learning: A Use
Case-Driven Analysis Approach
|
Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.
|
[
"['Sulaiman Aburakhia' 'Abdallah Shami' 'George K. Karagiannidis']"
] |
null | null |
2403.17210
| null | null |
http://arxiv.org/pdf/2403.17210v2
|
2024-03-27T21:47:49Z
|
2024-03-25T21:37:31Z
|
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug
Interactions
|
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potential to pave the way for creating and advancing innovative medications applicable in practical settings. However, existing DDI prediction models continue to face challenges related to generalization in extreme cases, robust feature extraction, and real-life application possibilities. We aim to address these challenges by leveraging the effectiveness of context-aware deep graph learning by introducing a novel framework named CADGL. Based on a customized variational graph autoencoder (VGAE), we capture critical structural and physio-chemical information using two context preprocessors for feature extraction from two different perspectives: local neighborhood and molecular context, in a heterogeneous graphical structure. Our customized VGAE consists of a graph encoder, a latent information encoder, and an MLP decoder. CADGL surpasses other state-of-the-art DDI prediction models, excelling in predicting clinically valuable novel DDIs, supported by rigorous case studies.
|
[
"['Azmine Toushik Wasi' 'Taki Hasan Rafi' 'Raima Islam' 'Serbetar Karlo'\n 'Dong-Kyu Chae']"
] |
null | null |
2403.17212
| null | null |
http://arxiv.org/pdf/2403.17212v1
|
2024-03-25T21:39:33Z
|
2024-03-25T21:39:33Z
|
Sanity Checks for Explanation Uncertainty
|
Explanations for machine learning models can be hard to interpret or be wrong. Combining an explanation method with an uncertainty estimation method produces explanation uncertainty. Evaluating explanation uncertainty is difficult. In this paper we propose sanity checks for uncertainty explanation methods, where a weight and data randomization tests are defined for explanations with uncertainty, allowing for quick tests to combinations of uncertainty and explanation methods. We experimentally show the validity and effectiveness of these tests on the CIFAR10 and California Housing datasets, noting that Ensembles seem to consistently pass both tests with Guided Backpropagation, Integrated Gradients, and LIME explanations.
|
[
"['Matias Valdenegro-Toro' 'Mihir Mulye']"
] |
null | null |
2403.17218
| null | null |
http://arxiv.org/pdf/2403.17218v1
|
2024-03-25T21:47:36Z
|
2024-03-25T21:47:36Z
|
A Comprehensive Study of the Capabilities of Large Language Models for
Vulnerability Detection
|
Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks. Vulnerability detection is of crucial importance to maintaining the security, integrity, and trustworthiness of software systems. Precise vulnerability detection requires reasoning about the code, making it a good case study for exploring the limits of LLMs' reasoning capabilities. Although recent work has applied LLMs to vulnerability detection using generic prompting techniques, their full capabilities for this task and the types of errors they make when explaining identified vulnerabilities remain unclear. In this paper, we surveyed eleven LLMs that are state-of-the-art in code generation and commonly used as coding assistants, and evaluated their capabilities for vulnerability detection. We systematically searched for the best-performing prompts, incorporating techniques such as in-context learning and chain-of-thought, and proposed three of our own prompting methods. Our results show that while our prompting methods improved the models' performance, LLMs generally struggled with vulnerability detection. They reported 0.5-0.63 Balanced Accuracy and failed to distinguish between buggy and fixed versions of programs in 76% of cases on average. By comprehensively analyzing and categorizing 287 instances of model reasoning, we found that 57% of LLM responses contained errors, and the models frequently predicted incorrect locations of buggy code and misidentified bug types. LLMs only correctly localized 6 out of 27 bugs in DbgBench, and these 6 bugs were predicted correctly by 70-100% of human participants. These findings suggest that despite their potential for other tasks, LLMs may fail to properly comprehend critical code structures and security-related concepts. Our data and code are available at https://figshare.com/s/78fe02e56e09ec49300b.
|
[
"['Benjamin Steenhoek' 'Md Mahbubur Rahman' 'Monoshi Kumar Roy'\n 'Mirza Sanjida Alam' 'Earl T. Barr' 'Wei Le']"
] |
null | null |
2403.17223
| null | null |
http://arxiv.org/pdf/2403.17223v1
|
2024-03-25T21:53:36Z
|
2024-03-25T21:53:36Z
|
Co-Occurring of Object Detection and Identification towards unlabeled
object discovery
|
In this paper, we propose a novel deep learning based approach for identifying co-occurring objects in conjunction with base objects in multilabel object categories. Nowadays, with the advancement in computer vision based techniques we need to know about co-occurring objects with respect to base object for various purposes. The pipeline of the proposed work is composed of two stages: in the first stage of the proposed model we detect all the bounding boxes present in the image and their corresponding labels, then in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we set base classes based on the maximum occurrences of the labels and build association rules and generate frequent patterns. These frequent patterns will show base classes and their corresponding co-occurring classes. We performed our experiments on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on public benchmark dataset is reported in Sec 4. Further we extend this work by considering all frequently objects as unlabeled and what if they are occluded as well.
|
[
"['Binay Kumar Singh' 'Niels Da Vitoria Lobo']"
] |
null | null |
2403.17224
| null | null |
http://arxiv.org/pdf/2403.17224v1
|
2024-03-25T21:56:02Z
|
2024-03-25T21:56:02Z
|
Uncertainty Quantification for Gradient-based Explanations in Neural
Networks
|
Explanation methods help understand the reasons for a model's prediction. These methods are increasingly involved in model debugging, performance optimization, and gaining insights into the workings of a model. With such critical applications of these methods, it is imperative to measure the uncertainty associated with the explanations generated by these methods. In this paper, we propose a pipeline to ascertain the explanation uncertainty of neural networks by combining uncertainty estimation methods and explanation methods. We use this pipeline to produce explanation distributions for the CIFAR-10, FER+, and California Housing datasets. By computing the coefficient of variation of these distributions, we evaluate the confidence in the explanation and determine that the explanations generated using Guided Backpropagation have low uncertainty associated with them. Additionally, we compute modified pixel insertion/deletion metrics to evaluate the quality of the generated explanations.
|
[
"['Mihir Mulye' 'Matias Valdenegro-Toro']"
] |
null | null |
2403.17231
| null | null |
http://arxiv.org/pdf/2403.17231v1
|
2024-03-25T22:17:51Z
|
2024-03-25T22:17:51Z
|
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from
Learned Hallucination
|
This paper presents a self-supervised learning method to safely learn a motion planner for ground robots to navigate environments with dense and dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict obstacles, classical motion planners may not be able to keep up with limited onboard computation. For learning-based planners, high-quality demonstrations are difficult to acquire for imitation learning while reinforcement learning becomes inefficient due to the high probability of collision during exploration. To safely and efficiently provide training data, the Learning from Hallucination (LfH) approaches synthesize difficult navigation environments based on past successful navigation experiences in relatively easy or completely open ones, but unfortunately cannot address dynamic obstacles. In our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and learn a novel latent distribution and sample dynamic obstacles from it, so the generated training data can be used to learn a motion planner to navigate in dynamic environments. Dyna-LfLH is evaluated on a ground robot in both simulated and physical environments and achieves up to 25% better success rate compared to baselines.
|
[
"['Saad Abdul Ghani' 'Zizhao Wang' 'Peter Stone' 'Xuesu Xiao']"
] |
null | null |
2403.17233
| null | null |
http://arxiv.org/pdf/2403.17233v1
|
2024-03-25T22:20:45Z
|
2024-03-25T22:20:45Z
|
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling
Process
|
We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process. Our proposed algorithm guides the exploration toward regions that demonstrate high empirical discrepancy between the observed data and an imperfect prior model of the dynamics derived from side information. Through numerical experiments, we demonstrate that this strategy explores regions of high discrepancy and accelerates learning while simultaneously reducing model uncertainty. We rigorously prove that our active learning algorithm yields a consistent estimate of the underlying dynamics by providing an explicit rate of convergence for the maximum predictive variance. We demonstrate the efficacy of our approach on an under-actuated pendulum system and on the half-cheetah MuJoCo environment.
|
[
"['Kevin S. Miller' 'Adam J. Thorpe' 'Ufuk Topcu']"
] |
null | null |
2403.17236
| null | null |
http://arxiv.org/pdf/2403.17236v1
|
2024-03-25T22:26:09Z
|
2024-03-25T22:26:09Z
|
Neural Image Compression with Quantization Rectifier
|
Neural image compression has been shown to outperform traditional image codecs in terms of rate-distortion performance. However, quantization introduces errors in the compression process, which can degrade the quality of the compressed image. Existing approaches address the train-test mismatch problem incurred during quantization, the random impact of quantization on the expressiveness of image features is still unsolved. This paper presents a novel quantization rectifier (QR) method for image compression that leverages image feature correlation to mitigate the impact of quantization. Our method designs a neural network architecture that predicts unquantized features from the quantized ones, preserving feature expressiveness for better image reconstruction quality. We develop a soft-to-predictive training technique to integrate QR into existing neural image codecs. In evaluation, we integrate QR into state-of-the-art neural image codecs and compare enhanced models and baselines on the widely-used Kodak benchmark. The results show consistent coding efficiency improvement by QR with a negligible increase in the running time.
|
[
"['Wei Luo' 'Bo Chen']"
] |
null | null |
2403.17238
| null | null |
http://arxiv.org/pdf/2403.17238v1
|
2024-03-25T22:39:20Z
|
2024-03-25T22:39:20Z
|
Temporal and Semantic Evaluation Metrics for Foundation Models in
Post-Hoc Analysis of Robotic Sub-tasks
|
Recent works in Task and Motion Planning (TAMP) show that training control policies on language-supervised robot trajectories with quality labeled data markedly improves agent task success rates. However, the scarcity of such data presents a significant hurdle to extending these methods to general use cases. To address this concern, we present an automated framework to decompose trajectory data into temporally bounded and natural language-based descriptive sub-tasks by leveraging recent prompting strategies for Foundation Models (FMs) including both Large Language Models (LLMs) and Vision Language Models (VLMs). Our framework provides both time-based and language-based descriptions for lower-level sub-tasks that comprise full trajectories. To rigorously evaluate the quality of our automatic labeling framework, we contribute an algorithm SIMILARITY to produce two novel metrics, temporal similarity and semantic similarity. The metrics measure the temporal alignment and semantic fidelity of language descriptions between two sub-task decompositions, namely an FM sub-task decomposition prediction and a ground-truth sub-task decomposition. We present scores for temporal similarity and semantic similarity above 90%, compared to 30% of a randomized baseline, for multiple robotic environments, demonstrating the effectiveness of our proposed framework. Our results enable building diverse, large-scale, language-supervised datasets for improved robotic TAMP.
|
[
"['Jonathan Salfity' 'Selma Wanna' 'Minkyu Choi' 'Mitch Pryor']"
] |
null | null |
2403.17239
| null | null |
http://arxiv.org/pdf/2403.17239v1
|
2024-03-25T22:39:47Z
|
2024-03-25T22:39:47Z
|
Manufacturing Service Capability Prediction with Graph Neural Networks
|
In the current landscape, the predominant methods for identifying manufacturing capabilities from manufacturers rely heavily on keyword matching and semantic matching. However, these methods often fall short by either overlooking valuable hidden information or misinterpreting critical data. Consequently, such approaches result in an incomplete identification of manufacturers' capabilities. This underscores the pressing need for data-driven solutions to enhance the accuracy and completeness of manufacturing capability identification. To address the need, this study proposes a Graph Neural Network-based method for manufacturing service capability identification over a knowledge graph. To enhance the identification performance, this work introduces a novel approach that involves aggregating information from the graph nodes' neighborhoods as well as oversampling the graph data, which can be effectively applied across a wide range of practical scenarios. Evaluations conducted on a Manufacturing Service Knowledge Graph and subsequent ablation studies demonstrate the efficacy and robustness of the proposed approach. This study not only contributes a innovative method for inferring manufacturing service capabilities but also significantly augments the quality of Manufacturing Service Knowledge Graphs.
|
[
"['Yunqing Li' 'Xiaorui Liu' 'Binil Starly']"
] |
null | null |
2403.17259
| null | null |
http://arxiv.org/pdf/2403.17259v1
|
2024-03-25T23:07:31Z
|
2024-03-25T23:07:31Z
|
Diffusion-based Negative Sampling on Graphs for Link Prediction
|
Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. To address these issues, we investigate a novel strategy of multi-level negative sampling that enables negative node generation with flexible and controllable ``hardness'' levels from the latent space. Our method, called Conditional Diffusion-based Multi-level Negative Sampling (DMNS), leverages the Markov chain property of diffusion models to generate negative nodes in multiple levels of variable hardness and reconcile them for effective graph link prediction. We further demonstrate that DMNS follows the sub-linear positivity principle for robust negative sampling. Extensive experiments on several benchmark datasets demonstrate the effectiveness of DMNS.
|
[
"['Trung-Kien Nguyen' 'Yuan Fang']"
] |
null | null |
2403.17266
| null | null |
http://arxiv.org/pdf/2403.17266v1
|
2024-03-25T23:19:19Z
|
2024-03-25T23:19:19Z
|
Exploring CausalWorld: Enhancing robotic manipulation via knowledge
transfer and curriculum learning
|
This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers. By employing reinforcement learning, we train an agent to acquire the necessary skills for proficient manipulation. To enhance the efficiency and effectiveness of the learning process, two knowledge transfer strategies, fine-tuning and curriculum learning, were utilized within the soft actor-critic architecture. Fine-tuning allows the agent to leverage pre-trained knowledge and adapt it to new tasks. Several variations like model transfer, policy transfer, and across-task transfer were implemented and evaluated. To eliminate the need for pretraining, curriculum learning decomposes the advanced task into simpler, progressive stages, mirroring how humans learn. The number of learning stages, the context of the sub-tasks, and the transition timing were found to be the critical design parameters. The key factors of two learning strategies and corresponding effects were explored in context-aware and context-unaware scenarios, enabling us to identify the scenarios where the methods demonstrate optimal performance, derive conclusive insights, and contribute to a broader range of learning-based engineering applications.
|
[
"['Xinrui Wang' 'Yan Jin']"
] |
null | null |
2403.17285
| null | null |
http://arxiv.org/pdf/2403.17285v1
|
2024-03-26T00:25:32Z
|
2024-03-26T00:25:32Z
|
An Analysis of Switchback Designs in Reinforcement Learning
|
This paper offers a detailed investigation of switchback designs in A/B testing, which alternate between baseline and new policies over time. Our aim is to thoroughly evaluate the effects of these designs on the accuracy of their resulting average treatment effect (ATE) estimators. We propose a novel "weak signal analysis" framework, which substantially simplifies the calculations of the mean squared errors (MSEs) of these ATEs in Markov decision process environments. Our findings suggest that (i) when the majority of reward errors are positively correlated, the switchback design is more efficient than the alternating-day design which switches policies in a daily basis. Additionally, increasing the frequency of policy switches tends to reduce the MSE of the ATE estimator. (ii) When the errors are uncorrelated, however, all these designs become asymptotically equivalent. (iii) In cases where the majority of errors are negative correlated, the alternating-day design becomes the optimal choice. These insights are crucial, offering guidelines for practitioners on designing experiments in A/B testing. Our analysis accommodates a variety of policy value estimators, including model-based estimators, least squares temporal difference learning estimators, and double reinforcement learning estimators, thereby offering a comprehensive understanding of optimal design strategies for policy evaluation in reinforcement learning.
|
[
"['Qianglin Wen' 'Chengchun Shi' 'Ying Yang' 'Niansheng Tang' 'Hongtu Zhu']"
] |
null | null |
2403.17287
| null | null |
http://arxiv.org/pdf/2403.17287v1
|
2024-03-26T00:33:49Z
|
2024-03-26T00:33:49Z
|
Not All Federated Learning Algorithms Are Created Equal: A Performance
Evaluation Study
|
Federated Learning (FL) emerged as a practical approach to training a model from decentralized data. The proliferation of FL led to the development of numerous FL algorithms and mechanisms. Many prior efforts have given their primary focus on accuracy of those approaches, but there exists little understanding of other aspects such as computational overheads, performance and training stability, etc. To bridge this gap, we conduct extensive performance evaluation on several canonical FL algorithms (FedAvg, FedProx, FedYogi, FedAdam, SCAFFOLD, and FedDyn) by leveraging an open-source federated learning framework called Flame. Our comprehensive measurement study reveals that no single algorithm works best across different performance metrics. A few key observations are: (1) While some state-of-the-art algorithms achieve higher accuracy than others, they incur either higher computation overheads (FedDyn) or communication overheads (SCAFFOLD). (2) Recent algorithms present smaller standard deviation in accuracy across clients than FedAvg, indicating that the advanced algorithms' performances are stable. (3) However, algorithms such as FedDyn and SCAFFOLD are more prone to catastrophic failures without the support of additional techniques such as gradient clipping. We hope that our empirical study can help the community to build best practices in evaluating FL algorithms.
|
[
"['Gustav A. Baumgart' 'Jaemin Shin' 'Ali Payani' 'Myungjin Lee'\n 'Ramana Rao Kompella']"
] |
null | null |
2403.17296
| null | null |
http://arxiv.org/pdf/2403.17296v1
|
2024-03-26T00:51:12Z
|
2024-03-26T00:51:12Z
|
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure
Lookup Table Computation
|
Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data owners secret-share their data between two servers that train and evaluate the model on the joint data. A significant source of inefficiency and inaccuracy in existing methods arises from using Yao's garbled circuits to compute non-linear activation functions. We propose new methods for computing non-linear functions based on secret-shared lookup tables, offering both computational efficiency and improved accuracy. Beyond introducing leakage-free techniques, we initiate the exploration of relaxed security measures for privacy-preserving machine learning. Instead of claiming that the servers gain no knowledge during the computation, we contend that while some information is revealed about access patterns to lookup tables, it maintains epsilon-dX-privacy. Leveraging this relaxation significantly reduces the computational resources needed for training. We present new cryptographic protocols tailored to this relaxed security paradigm and define and analyze the leakage. Our evaluations show that our logistic regression protocol is up to 9x faster, and the neural network training is up to 688x faster than SecureML. Notably, our neural network achieves an accuracy of 96.6% on MNIST in 15 epochs, outperforming prior benchmarks that capped at 93.4% using the same architecture.
|
[
"['Hamza Saleem' 'Amir Ziashahabi' 'Muhammad Naveed' 'Salman Avestimehr']"
] |
null | null |
2403.17308
| null | null |
http://arxiv.org/pdf/2403.17308v1
|
2024-03-26T01:29:46Z
|
2024-03-26T01:29:46Z
|
Neural Multimodal Topic Modeling: A Comprehensive Evaluation
|
Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This paper presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing both text and images. In the process, we propose two novel topic modeling solutions and two novel evaluation metrics. Overall, our evaluation on an unprecedented rich and diverse collection of datasets indicates that both of our models generate coherent and diverse topics. Nevertheless, the extent to which one method outperforms the other depends on the metrics and dataset combinations, which suggests further exploration of hybrid solutions in the future. Notably, our succinct human evaluation aligns with the outcomes determined by our proposed metrics. This alignment not only reinforces the credibility of our metrics but also highlights the potential for their application in guiding future multimodal topic modeling endeavors.
|
[
"['Felipe González-Pizarro' 'Giuseppe Carenini']"
] |
null | null |
2403.17312
| null | null |
http://arxiv.org/pdf/2403.17312v1
|
2024-03-26T01:46:34Z
|
2024-03-26T01:46:34Z
|
ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV
Caching
|
The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT, which have come to dominate a broad range of NLP tasks. Despite their superior accuracy, LLMs present unique challenges in practical inference, concerning the compute and memory-intensive nature. Thanks to the autoregressive characteristic of LLM inference, KV caching for the attention layers in Transformers can effectively accelerate LLM inference by substituting quadratic-complexity computation with linear-complexity memory accesses. Yet, this approach requires increasing memory as demand grows for processing longer sequences. The overhead leads to reduced throughput due to I/O bottlenecks and even out-of-memory errors, particularly on resource-constrained systems like a single commodity GPU. In this paper, we propose ALISA, a novel algorithm-system co-design solution to address the challenges imposed by KV caching. On the algorithm level, ALISA prioritizes tokens that are most important in generating a new token via a Sparse Window Attention (SWA) algorithm. SWA introduces high sparsity in attention layers and reduces the memory footprint of KV caching at negligible accuracy loss. On the system level, ALISA employs three-phase token-level dynamical scheduling and optimizes the trade-off between caching and recomputation, thus maximizing the overall performance in resource-constrained systems. In a single GPU-CPU system, we demonstrate that under varying workloads, ALISA improves the throughput of baseline systems such as FlexGen and vLLM by up to 3X and 1.9X, respectively.
|
[
"['Youpeng Zhao' 'Di Wu' 'Jun Wang']"
] |
null | null |
2403.17329
| null | null |
http://arxiv.org/pdf/2403.17329v2
|
2024-06-27T06:19:01Z
|
2024-03-26T02:24:32Z
|
Deep Support Vectors
|
Deep learning has achieved tremendous success. nj{However,} unlike SVMs, which provide direct decision criteria and can be trained with a small dataset, it still has significant weaknesses due to its requirement for massive datasets during training and the black-box characteristics on decision criteria. nj{This paper addresses} these issues by identifying support vectors in deep learning models. To this end, we propose the DeepKKT condition, an adaptation of the traditional Karush-Kuhn-Tucker (KKT) condition for deep learning models, and confirm that generated Deep Support Vectors (DSVs) using this condition exhibit properties similar to traditional support vectors. This allows us to apply our method to few-shot dataset distillation problems and alleviate the black-box characteristics of deep learning models. Additionally, we demonstrate that the DeepKKT condition can transform conventional classification models into generative models with high fidelity, particularly as latent jh{generative} models using class labels as latent variables. We validate the effectiveness of DSVs nj{using common datasets (ImageNet, CIFAR10 nj{and} CIFAR100) on the general architectures (ResNet and ConvNet)}, proving their practical applicability. (See Fig.~ref{fig:generated})
|
[
"['Junhoo Lee' 'Hyunho Lee' 'Kyomin Hwang' 'Nojun Kwak']"
] |
null | null |
2403.17333
| null | null |
http://arxiv.org/pdf/2403.17333v1
|
2024-03-26T02:33:36Z
|
2024-03-26T02:33:36Z
|
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
|
Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as healthcare, education and employment. Since they are used in numerous sensitive environments and make decisions that can be life altering, potential biased outcomes are a pressing matter. Developers should ensure that such models don't manifest any unexpected discriminatory practices like partiality for certain genders, ethnicities or disabled people. With the ubiquitous dissemination of AI systems, researchers and practitioners are becoming more aware of unfair models and are bound to mitigate bias in them. Significant research has been conducted in addressing such issues to ensure models don't intentionally or unintentionally perpetuate bias. This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems. We explore the different definitions of fairness existing in the current literature. We create a comprehensive taxonomy by categorizing different types of bias and investigate cases of biased AI in different application domains. A thorough study is conducted of the approaches and techniques employed by researchers to mitigate bias in AI models. Moreover, we also delve into the impact of biased models on user experience and the ethical considerations to contemplate when developing and deploying such models. We hope this survey helps researchers and practitioners understand the intricate details of fairness and bias in AI systems. By sharing this thorough survey, we aim to promote additional discourse in the domain of equitable and responsible AI.
|
[
"['Tahsin Alamgir Kheya' 'Mohamed Reda Bouadjenek' 'Sunil Aryal']"
] |
null | null |
2403.17343
| null | null |
http://arxiv.org/pdf/2403.17343v3
|
2024-03-28T21:28:00Z
|
2024-03-26T03:05:20Z
|
Residual-based Language Models are Free Boosters for Biomedical Imaging
|
In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from established methodologies by utilizing a frozen transformer block, extracted from pre-trained LLMs, as an innovative encoder layer for the direct processing of visual tokens. This strategy represents a significant departure from the standard multi-modal vision-language frameworks, which typically hinge on language-driven prompts and inputs. We found that these LLMs could boost performance across a spectrum of biomedical imaging applications, including both 2D and 3D visual classification tasks, serving as plug-and-play boosters. More interestingly, as a byproduct, we found that the proposed framework achieved superior performance, setting new state-of-the-art results on extensive, standardized datasets in MedMNIST-2D and 3D. Through this work, we aim to open new avenues for employing LLMs in biomedical imaging and enriching the understanding of their potential in this specialized domain.
|
[
"['Zhixin Lai' 'Jing Wu' 'Suiyao Chen' 'Yucheng Zhou' 'Naira Hovakimyan']"
] |
null | null |
2403.17351
| null | null |
http://arxiv.org/pdf/2403.17351v1
|
2024-03-26T03:29:42Z
|
2024-03-26T03:29:42Z
|
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural
Network
|
Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance. Current studies on graph heterophily mainly focus on aggregation calibration or neighbor extension and address the heterophily issue by utilizing node features or structural information to improve GNN representations. In this paper, we propose and demonstrate that the valuable semantic information inherent in heterophily can be utilized effectively in graph learning by investigating the distribution of neighbors for each individual node within the graph. The theoretical analysis is carried out to demonstrate the efficacy of the idea in enhancing graph learning. Based on this analysis, we propose HiGNN, an innovative approach that constructs an additional new graph structure, that integrates heterophilous information by leveraging node distribution to enhance connectivity between nodes that share similar semantic characteristics. We conduct empirical assessments on node classification tasks using both homophilous and heterophilous benchmark datasets and compare HiGNN to popular GNN baselines and SoTA methods, confirming the effectiveness in improving graph representations. In addition, by incorporating heterophilous information, we demonstrate a notable enhancement in existing GNN-based approaches, and the homophily degree across real-world datasets, thus affirming the efficacy of our approach.
|
[
"['Yilun Zheng' 'Jiahao Xu' 'Lihui Chen']"
] |
null | null |
2403.17353
| null | null |
http://arxiv.org/pdf/2403.17353v1
|
2024-03-26T03:32:45Z
|
2024-03-26T03:32:45Z
|
Multi-Objective Trajectory Planning with Dual-Encoder
|
Time-jerk optimal trajectory planning is crucial in advancing robotic arms' performance in dynamic tasks. Traditional methods rely on solving complex nonlinear programming problems, bringing significant delays in generating optimized trajectories. In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning. Firstly, we introduce a dual-encoder based transformer model to establish a good preliminary trajectory. This trajectory is subsequently refined through sequential quadratic programming to improve its optimality and robustness. Our approach outperforms the state-of-the-art by up to 79.72% in reducing trajectory planning time. Compared with existing methods, our method shrinks the optimality gap with the objective function value decreasing by up to 29.9%.
|
[
"['Beibei Zhang' 'Tian Xiang' 'Chentao Mao' 'Yuhua Zheng' 'Shuai Li'\n 'Haoyi Niu' 'Xiangming Xi' 'Wenyuan Bai' 'Feng Gao']"
] |
null | null |
2403.17364
| null | null |
http://arxiv.org/pdf/2403.17364v1
|
2024-03-26T04:02:09Z
|
2024-03-26T04:02:09Z
|
A Moreau Envelope Approach for LQR Meta-Policy Estimation
|
We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a finite set of realizations of the uncertain system, to define a meta-policy efficiently adjustable to new realizations. Moreover, we design an algorithm to find an approximate first-order stationary point of the meta-LQR cost function. Numerical results show that the proposed approach outperforms naive averaging of controllers on new realizations of the linear system. We also provide empirical evidence that our method has better sample complexity than Model-Agnostic Meta-Learning (MAML) approaches.
|
[
"['Ashwin Aravind' 'Mohammad Taha Toghani' 'César A. Uribe']"
] |
null | null |
2403.17373
| null | null |
http://arxiv.org/pdf/2403.17373v1
|
2024-03-26T04:27:56Z
|
2024-03-26T04:27:56Z
|
AIDE: An Automatic Data Engine for Object Detection in Autonomous
Driving
|
Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
|
[
"['Mingfu Liang' 'Jong-Chyi Su' 'Samuel Schulter' 'Sparsh Garg'\n 'Shiyu Zhao' 'Ying Wu' 'Manmohan Chandraker']"
] |
null | null |
2403.17377
| null | null |
http://arxiv.org/pdf/2403.17377v1
|
2024-03-26T04:49:11Z
|
2024-03-26T04:49:11Z
|
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
|
Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration. In this paper, we propose a novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG is designed to progressively enhance the structure of samples throughout the denoising process. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, by considering the self-attention mechanisms' ability to capture structural information, and guiding the denoising process away from these degraded samples. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional scenarios. Moreover, PAG significantly improves the baseline performance in various downstream tasks where existing guidances such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and image restoration such as inpainting and deblurring.
|
[
"['Donghoon Ahn' 'Hyoungwon Cho' 'Jaewon Min' 'Wooseok Jang' 'Jungwoo Kim'\n 'SeonHwa Kim' 'Hyun Hee Park' 'Kyong Hwan Jin' 'Seungryong Kim']"
] |
null | null |
2403.17379
| null | null |
http://arxiv.org/pdf/2403.17379v1
|
2024-02-22T22:34:06Z
|
2024-02-22T22:34:06Z
|
Exploring and Applying Audio-Based Sentiment Analysis in Music
|
Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text as deeply as they do in music. The ability of a computational model to interpret musical emotions is largely unexplored and could have implications and uses in therapy and musical queuing. In this paper, two individual tasks are addressed. This study seeks to (1) predict the emotion of a musical clip over time and (2) determine the next emotion value after the music in a time series to ensure seamless transitions. Utilizing data from the Emotions in Music Database, which contains clips of songs selected from the Free Music Archive annotated with levels of valence and arousal as reported on Russel's circumplex model of affect by multiple volunteers, models are trained for both tasks. Overall, the performance of these models reflected that they were able to perform the tasks they were designed for effectively and accurately.
|
[
"['Etash Jhanji']"
] |
null | null |
2403.17381
| null | null |
http://arxiv.org/pdf/2403.17381v1
|
2024-03-26T04:59:27Z
|
2024-03-26T04:59:27Z
|
Application-Driven Innovation in Machine Learning
|
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.
|
[
"['David Rolnick' 'Alan Aspuru-Guzik' 'Sara Beery' 'Bistra Dilkina'\n 'Priya L. Donti' 'Marzyeh Ghassemi' 'Hannah Kerner' 'Claire Monteleoni'\n 'Esther Rolf' 'Milind Tambe' 'Adam White']"
] |
null | null |
2403.17404
| null | null |
http://arxiv.org/pdf/2403.17404v1
|
2024-03-26T05:48:02Z
|
2024-03-26T05:48:02Z
|
Generalization Error Analysis for Sparse Mixture-of-Experts: A
Preliminary Study
|
Mixture-of-Experts (MoE) represents an ensemble methodology that amalgamates predictions from several specialized sub-models (referred to as experts). This fusion is accomplished through a router mechanism, dynamically assigning weights to each expert's contribution based on the input data. Conventional MoE mechanisms select all available experts, incurring substantial computational costs. In contrast, Sparse Mixture-of-Experts (Sparse MoE) selectively engages only a limited number, or even just one expert, significantly reducing computation overhead while empirically preserving, and sometimes even enhancing, performance. Despite its wide-ranging applications and these advantageous characteristics, MoE's theoretical underpinnings have remained elusive. In this paper, we embark on an exploration of Sparse MoE's generalization error concerning various critical factors. Specifically, we investigate the impact of the number of data samples, the total number of experts, the sparsity in expert selection, the complexity of the routing mechanism, and the complexity of individual experts. Our analysis sheds light on textit{how textbf{sparsity} contributes to the MoE's generalization}, offering insights from the perspective of classical learning theory.
|
[
"['Jinze Zhao' 'Peihao Wang' 'Zhangyang Wang']"
] |
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