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Title: Mixture-of-Rookies: Saving DNN Computations by Predicting ReLU Outputs Abstract: Deep Neural Networks (DNNs) are widely used in many applications domains. However, they require a vast amount of computations and memory accesses to deliver outstanding accuracy. In this paper, we propose a scheme to predict whether... |
Title: AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks Abstract: Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper introduces a novel data-driven predictive model based on Trans... |
Title: SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics Abstract: Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Geant4 are computationally intensive, and cu... |
Title: CMOS Circuits for Shape-Based Analog Machine Learning Abstract: While analog computing is attractive for implementing machine learning (ML) processors, the paradigm requires chip-in-the-loop training for every processor to alleviate artifacts due to device mismatch and device non-linearity. Speeding up chip-in-t... |
Title: On characterizations of learnability with computable learners Abstract: We study computable PAC (CPAC) learning as introduced by Agarwal et al. (2020). First, we consider the main open question of finding characterizations of proper and improper CPAC learning. We give a characterization of a closely related noti... |
Title: Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment Abstract: To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision re... |
Title: Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings Abstract: Many popular algorithmic fairness measures depend on the joint distribution of predictions, outcomes, and a sensitive feature like race or gender. These measures are sensitive to distr... |
Title: Equivariance Regularization for Image Reconstruction Abstract: In this work, we propose Regularization-by-Equivariance (REV), a novel structure-adaptive regularization scheme for solving imaging inverse problems under incomplete measurements. This regularization scheme utilizes the equivariant structure in the p... |
Title: PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty Abstract: Learning data representations under uncertainty is an important task that emerges in numerous machine learning applications. However, uncertainty quantification (UQ) techniques are computationally intensive and become prohibitively ex... |
Title: Controlling the Complexity and Lipschitz Constant improves polynomial nets Abstract: While the class of Polynomial Nets demonstrates comparable performance to neural networks (NN), it currently has neither theoretical generalization characterization nor robustness guarantees. To this end, we derive new complexit... |
Title: Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case Abstract: With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help ... |
Title: Cross-speaker style transfer for text-to-speech using data augmentation Abstract: We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data augmentation via voice conversion. We assume to have a corpus of neutral non-expressive data from a target speaker and supporting conversati... |
Title: Backpropagation Clipping for Deep Learning with Differential Privacy Abstract: We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each trainable layer's inputs (during the forward pass) and i... |
Title: Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data Abstract: We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. Given heterogeneous time series data consisting of operation cycle signals and sensor signals, we aim at disc... |
Title: Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals Abstract: We consider the well-studied problem of learning intersections of halfspaces under the Gaussian distribution in the challenging \emph{agnostic learning} model. Recent work of Diakon... |
Title: AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems Abstract: Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method has been recognized as a standard approach to quantum transport simulations. Although it achieves superiority in si... |
Title: Adaptively Exploiting d-Separators with Causal Bandits Abstract: Multi-armed bandit problems provide a framework to identify the optimal intervention over a sequence of repeated experiments. Without additional assumptions, minimax optimal performance (measured by cumulative regret) is well-understood. With acces... |
Title: Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints Abstract: In a first part, we present a mathematical analysis of a general methodology of a probabilistic learning inference that allows for estimating a posterior probabi... |
Title: Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace Abstract: In this study, a real-time dispatching algorithm based on reinforcement learning is proposed and for the first time, is deployed in large scale. Current dispatching methods in ridehailing platforms... |
Title: Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products Abstract: We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm. Here, given access to a matrix $A$ through matrix-vector products, an accuracy parameter $\epsilon$, and a target rank $k$, ... |
Title: Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection Abstract: In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: the prediction bounding... |
Title: Deep Learning for Computational Cytology: A Survey Abstract: Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening. Recently, an increasing number of dee... |
Title: Group-Agent Reinforcement Learning Abstract: It can largely benefit the reinforcement learning process of each agent if multiple agents perform their separate reinforcement learning tasks cooperatively. Different from multi-agent reinforcement learning where multiple agents are in a common environment and should... |
Title: Quantization in Layer's Input is Matter Abstract: In this paper, we will show that the quantization in layer's input is more important than parameters' quantization for loss function. And the algorithm which is based on the layer's input quantization error is better than hessian-based mixed precision layout algo... |
Title: Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction Abstract: Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all data on each platform can be used f... |
Title: Deep learning for drug repurposing: methods, databases, and applications Abstract: Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (C... |
Title: EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction Abstract: Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug enginee... |
Title: Feature-level augmentation to improve robustness of deep neural networks to affine transformations Abstract: Recent studies revealed that convolutional neural networks do not generalize well to small image transformations, e.g. rotations by a few degrees or translations of a few pixels. To improve the robustness... |
Title: Effective classification of ecg signals using enhanced convolutional neural network in iot Abstract: In this paper, a novel ECG monitoring approach based on IoT technology is suggested. This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy a... |
Title: DeepCENT: Prediction of Censored Event Time via Deep Learning Abstract: With the rapid advances of deep learning, many computational methods have been developed to analyze nonlinear and complex right censored data via deep learning approaches. However, the majority of the methods focus on predicting survival fun... |
Title: Advanced sleep spindle identification with neural networks Abstract: Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identifi... |
Title: Optimal reservoir computers for forecasting systems of nonlinear dynamics Abstract: Prediction and analysis of systems of nonlinear dynamics is crucial in many applications. Here, we study characteristics and optimization of reservoir computing, a machine learning technique that has gained attention as a suitabl... |
Title: Machine Learning and Data Science: Foundations, Concepts, Algorithms, and Tools Abstract: Today, data is a fuel for businesses to gain important insights and improve their performance. There is no industry in the world today that does not use data. But who will get this insight? Who processes all the raw data? E... |
Title: Design of Flexible Meander Line Antenna for Healthcare for Wireless Medical Body Area Networks Abstract: A flexible meander line monopole antenna (MMA) is presented in this paper. The antenna can be worn for on-and off-body applications. The overall dimension of the MMA is 37 mm x 50 mm x2.37 mm3. The MMA was ma... |
Title: Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units Abstract: Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition f... |
Title: Efficacy of Transformer Networks for Classification of Raw EEG Data Abstract: With the unprecedented success of transformer networks in natural language processing (NLP), recently, they have been successfully adapted to areas like computer vision, generative adversarial networks (GAN), and reinforcement learning... |
Title: Application of the Affinity Propagation Clustering Technique to obtain traffic accident clusters at macro, meso, and micro levels Abstract: Accident grouping is a crucial step in identifying accident-prone locations. Among the different accident grouping modes, clustering methods present excellent performance fo... |
Title: Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network Abstract: The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on ... |
Title: Remote Contextual Bandits Abstract: We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized... |
Title: Discovering Quantum Phase Transitions with Fermionic Neural Networks Abstract: Deep neural networks have been extremely successful as highly accurate wave function ans\"atze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such ansatz, FermiNet, to calculations ... |
Title: Understanding Rare Spurious Correlations in Neural Networks Abstract: Neural networks are known to use spurious correlations such as background information for classification. While prior work has looked at spurious correlations that are widespread in the training data, in this work, we investigate how sensitive... |
Title: Bayes Optimal Algorithm is Suboptimal in Frequentist Best Arm Identification Abstract: We consider the fixed-budget best arm identification problem with Normal rewards. In this problem, the forecaster is given $K$ arms (treatments) and $T$ time steps. The forecaster attempts to find the best arm in terms of the ... |
Title: P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints Abstract: We develop a class of mixed-integer formulations for disjunctive constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength. The main idea is to c... |
Title: Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment Abstract: For soft continuum arms, visual servoing is a popular control strategy that relies on visual feedback to close the control loop. However, robust visual servoing is challenging as it requires reliable feature extraction f... |
Title: Zero Shot Learning for Predicting Energy Usage of Buildings in Sustainable Design Abstract: The 2030 Challenge is aimed at making all new buildings and major renovations carbon neutral by 2030. One of the potential solutions to meet this challenge is through innovative sustainable design strategies. For developi... |
Title: Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers Abstract: Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of networks with hundreds of thousan... |
Title: Deadwooding: Robust Global Pruning for Deep Neural Networks Abstract: The ability of Deep Neural Networks to approximate highly complex functions is key to their success. This benefit, however, comes at the expense of a large model size, which challenges its deployment in resource-constrained environments. Pruni... |
Title: F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization Abstract: Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and full-preci... |
Title: ChemicalX: A Deep Learning Library for Drug Pair Scoring Abstract: In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring mod... |
Title: REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer Abstract: A popular paradigm in robotic learning is to train a policy from scratch for every new robot. This is not only inefficient but also often impractical for complex robots. In this work, we consider the problem of transferring a p... |
Title: Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression Abstract: We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning for causal inference, a wide ra... |
Title: Monotone Learning Abstract: The amount of training-data is one of the key factors which determines the generalization capacity of learning algorithms. Intuitively, one expects the error rate to decrease as the amount of training-data increases. Perhaps surprisingly, natural attempts to formalize this intuition g... |
Title: Adaptive and Robust Multi-task Learning Abstract: We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among th... |
Title: Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel Abstract: A biological neural network in the cortex forms a neural field. Neurons in the field have their own receptive fields, and connection weights between two neurons are random but highly correlated when they are in close... |
Title: Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials Abstract: Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently-known topological materials hav... |
Title: Conditional Diffusion Probabilistic Model for Speech Enhancement Abstract: Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs. While generative models have shown strong potential in speech synthesis, they are ... |
Title: Characterizing, Detecting, and Predicting Online Ban Evasion Abstract: Moderators and automated methods enforce bans on malicious users who engage in disruptive behavior. However, malicious users can easily create a new account to evade such bans. Previous research has focused on other forms of online deception,... |
Title: Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks Abstract: We give superpolynomial statistical query (SQ) lower bounds for learning two-hidden-layer ReLU networks with respect to Gaussian inputs in the standard (noise-free) model. No general SQ lower bounds were known for learning ReLU networ... |
Title: Locating and Editing Factual Associations in GPT Abstract: We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identify... |
Title: Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging Abstract: Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model... |
Title: Towards a Guideline for Evaluation Metrics in Medical Image Segmentation Abstract: In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demonstrated that these models have powerful predictio... |
Title: Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis Abstract: Our universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work we develop Translation and Rotation Equivariant Normalizing Flow (TRENF), a generative No... |
Title: On One-Bit Quantization Abstract: We consider the one-bit quantizer that minimizes the mean squared error for a source living in a real Hilbert space. The optimal quantizer is a projection followed by a thresholding operation, and we provide methods for identifying the optimal direction along which to project. A... |
Title: Universal Learning Waveform Selection Strategies for Adaptive Target Tracking Abstract: Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cram\... |
Title: Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient Abstract: The problem of human trust in artificial intelligence is one of the most fundamental problems in applied machine learning. Our processes for evaluating AI trustworthiness have substanti... |
Title: Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks Abstract: We hypothesize that due to the greedy nature of learning in multi-modal deep neural networks, these models tend to rely on just one modality while under-fitting the other modalities. Such behavior is counter... |
Title: Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization Abstract: Large-scale machine learning systems often involve data distributed across a collection of users. Federated optimization algorithms leverage this structure by communicating model updates to a central server, rather than entir... |
Title: Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs Abstract: Several services for people with visual disabilities have emerged recently due to achievements in Assistive Technologies and Artificial Intelligence areas. Despite the g... |
Title: Factored World Models for Zero-Shot Generalization in Robotic Manipulation Abstract: World models for environments with many objects face a combinatorial explosion of states: as the number of objects increases, the number of possible arrangements grows exponentially. In this paper, we learn to generalize over ro... |
Title: Dynamic Background Subtraction by Generative Neural Networks Abstract: Background subtraction is a significant task in computer vision and an essential step for many real world applications. One of the challenges for background subtraction methods is dynamic background, which constitute stochastic movements in s... |
Title: Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning Abstract: In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to com... |
Title: Coded ResNeXt: a network for designing disentangled information paths Abstract: To avoid treating neural networks as highly complex black boxes, the deep learning research community has tried to build interpretable models allowing humans to understand the decisions taken by the model. Unfortunately, the focus is... |
Title: Development and Validation of an AI-Driven Model for the La Rance Tidal Barrage: A Generalisable Case Study Abstract: In this work, an AI-Driven (autonomous) model representation of the La Rance tidal barrage was developed using novel parametrisation and Deep Reinforcement Learning (DRL) techniques. Our model re... |
Title: Domain Adversarial Training: A Game Perspective Abstract: The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a game theoretical perspective. Defining optimal solutions in domain-adversa... |
Title: DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI Abstract: Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for... |
Title: Including Facial Expressions in Contextual Embeddings for Sign Language Generation Abstract: State-of-the-art sign language generation frameworks lack expressivity and naturalness which is the result of only focusing manual signs, neglecting the affective, grammatical and semantic functions of facial expressions... |
Title: Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks Abstract: This thesis develops data-driven machine learning algorithms to managing and optimizing the next-generation highly complex cyberphysical systems, which desperately need ground-breaking control, monitoring, and d... |
Title: Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass Abstract: It is estimated that around 70 million people worldwide are affected by a speech disorder called stuttering. With recent advances in Automatic Speech Recognition (ASR), voice assistants are increasingly useful in our everyday li... |
Title: Neural Architecture Search for Energy Efficient Always-on Audio Models Abstract: Mobile and edge computing devices for always-on audio classification require energy-efficient neural network architectures. We present a neural architecture search (NAS) that optimizes accuracy, energy efficiency and memory usage. T... |
Title: PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition Abstract: We propose PARSE, a novel semi-supervised architecture for learning strong EEG representations for emotion recognition. To reduce the potential distribution mismatch between the large amounts of unlabel... |
Title: Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning Abstract: When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people... |
Title: Learning Temporal Rules from Noisy Timeseries Data Abstract: Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose higher level composite events. Examples of a composite event are a patient's ... |
Title: Regularized Q-learning Abstract: Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develops a new Q-learning algorithm ... |
Title: A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data Abstract: Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when). As in most geospatial analysis problems, besides high-dimensio... |
Title: A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability Abstract: We study the problem of semi-supervised learning of an adversarially-robust predictor in the PAC model, where the learner has access to both labeled and unlabeled examples. The sample complexity in semi-supervised learning has ... |
Title: Posterior Consistency for Bayesian Relevance Vector Machines Abstract: Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. Chakraborty et al. (2012) did a full hierarchical Bayesian analysis of nonlinear regression in such s... |
Title: Understanding Curriculum Learning in Policy Optimization for Solving Combinatorial Optimization Problems Abstract: Over the recent years, reinforcement learning (RL) has shown impressive performance in finding strategic solutions for game environments, and recently starts to show promising results in solving com... |
Title: A Survey on Programmatic Weak Supervision Abstract: Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved remarkable success in easing the manual labeling bottleneck by programmatically s... |
Title: Dual Task Framework for Improving Persona-grounded Dialogue Dataset Abstract: This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly depend on the raw crowdsourced benchmark datasets such as ... |
Title: Minimax Regret Optimization for Robust Machine Learning under Distribution Shift Abstract: In this paper, we consider learning scenarios where the learned model is evaluated under an unknown test distribution which potentially differs from the training distribution (i.e. distribution shift). The learner has acce... |
Title: Invariance Principle Meets Out-of-Distribution Generalization on Graphs Abstract: Despite recent developments in using the invariance principle from causality to enable out-of-distribution (OOD) generalization on Euclidean data, e.g., images, studies on graph data are limited. Different from images, the complex ... |
Title: Computational-Statistical Gaps in Reinforcement Learning Abstract: Reinforcement learning with function approximation has recently achieved tremendous results in applications with large state spaces. This empirical success has motivated a growing body of theoretical work proposing necessary and sufficient condit... |
Title: Fast Rates in Pool-Based Batch Active Learning Abstract: We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle. Sampling labels in batches is highly desirable in practice due to the smaller number of interactive rounds with the labeling oracle (of... |
Title: ACORT: A Compact Object Relation Transformer for Parameter Efficient Image Captioning Abstract: Recent research that applies Transformer-based architectures to image captioning has resulted in state-of-the-art image captioning performance, capitalising on the success of Transformers on natural language tasks. Un... |
Title: Robust estimation algorithms don't need to know the corruption level Abstract: Real data are rarely pure. Hence the past half-century has seen great interest in robust estimation algorithms that perform well even when part of the data is corrupt. However, their vast majority approach optimal accuracy only when g... |
Title: Conditional Contrastive Learning with Kernel Abstract: Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender ... |
Title: Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning Abstract: We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoder... |
Title: Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers Abstract: Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibility of backdoor attacks against malware classifiers,... |
Title: Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion Abstract: In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently. The framework consists of a policy network which outputs the desired jo... |
Title: Noise Augmentation Is All You Need For FGSM Fast Adversarial Training: Catastrophic Overfitting And Robust Overfitting Require Different Augmentation Abstract: Adversarial training (AT) and its variants are the most effective approaches for obtaining adversarially robust models. A unique characteristic of AT is ... |
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