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Title: Unsupervised Probabilistic Models for Sequential Electronic Health Records Abstract: We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and lab... |
Title: Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees Abstract: We develop a simple and unified framework for nonlinear variable selection that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (... |
Title: Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference Abstract: Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to s... |
Title: Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation Abstract: Recognizing human locomotion intent and activities is important for controlling the wearable robots while walking in complex environments. However, human-robot interface signals are usually user-dependent, ... |
Title: Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts Abstract: The incorporation of cutting planes within the branch-and-bound algorithm, known as branch-and-cut, forms the backbone of modern integer programming solvers. These solvers are the foremost method for solving discret... |
Title: XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding Abstract: Transformer-based models are widely used in natural language understanding (NLU) tasks, and multimodal transformers have been effective in visual-language tasks. This study explores distilling visua... |
Title: Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities Abstract: Graph neural networks have emerged as a leading architecture for many graph-level tasks such as graph classification and graph generation with a notable improvement. Among these tasks, graph pooling is an essential compone... |
Title: Knowledgebra: An Algebraic Learning Framework for Knowledge Graph Abstract: Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG dataset... |
Title: Crowd counting with crowd attention convolutional neural network Abstract: Crowd counting is a challenging problem due to the scene complexity and scale variation. Although deep learning has achieved great improvement in crowd counting, scene complexity affects the judgement of these methods and they usually reg... |
Title: A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations Abstract: We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through... |
Title: Anomalous Sound Detection Based on Machine Activity Detection Abstract: We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active. First, we train a model that detects machine activity by usin... |
Title: Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces Abstract: Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique... |
Title: Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network Abstract: This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The a... |
Title: Towards Building a Personalized Dialogue Generator via Implicit User Persona Detection Abstract: Current works in the generation of personalized dialogue primarily contribute to the agent avoiding contradictory persona and driving the response more informative. However, we found that the generated responses from... |
Title: Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning Abstract: Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not c... |
Title: Crowd counting with segmentation attention convolutional neural network Abstract: Deep learning occupies an undisputed dominance in crowd counting. In this paper, we propose a novel convolutional neural network (CNN) architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposeSegC... |
Title: Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method Abstract: Email is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occ... |
Title: Characterizing metastable states with the help of machine learning Abstract: Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature is becoming increasingly challenging. In this paper, we first us... |
Title: Deep learning model solves change point detection for multiple change types Abstract: A change points detection aims to catch an abrupt disorder in data distribution. Common approaches assume that there are only two fixed distributions for data: one before and another after a change point. Real-world data are ri... |
Title: SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing Abstract: Crowd counting based on density maps is generally regarded as a regression task.Deep learning is used to learn the mapping between image content and crowd density distribution. Although great success has been achieved,... |
Title: End-to-End Sensitivity-Based Filter Pruning Abstract: In this paper, we present a novel sensitivity-based filter pruning algorithm (SbF-Pruner) to learn the importance scores of filters of each layer end-to-end. Our method learns the scores from the filter weights, enabling it to account for the correlations bet... |
Title: Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks Abstract: We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network's architecture to inform the network of the physic... |
Title: Universal approximation property of invertible neural networks Abstract: Invertible neural networks (INNs) are neural network architectures with invertibility by design. Thanks to their invertibility and the tractability of Jacobian, INNs have various machine learning applications such as probabilistic modeling,... |
Title: Safe Reinforcement Learning Using Black-Box Reachability Analysis Abstract: Reinforcement learning (RL) is capable of sophisticated motion planning and control for robots in uncertain environments. However, state-of-the-art deep RL approaches typically lack safety guarantees, especially when the robot and enviro... |
Title: An interpretable machine learning approach for ferroalloys consumptions Abstract: This paper is devoted to a practical method for ferroalloys consumption modeling and optimization. We consider the problem of selecting the optimal process control parameters based on the analysis of historical data from sensors. W... |
Title: Experimentally realized memristive memory augmented neural network Abstract: Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be ... |
Title: The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants Abstract: Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm could solve the problem is henc... |
Title: Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm Abstract: This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the \textit{mask region proposal convolutional neural network} (Mask R... |
Title: INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold Abstract: Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks. BNNs, on the other hand, suffer from information loss because binary activations are limited to ... |
Title: Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters Abstract: Natural Language Inference (NLI) has been extensively studied by the NLP community as a framework for estimating the semantic relation between sentence pairs. While early work identified certain biases in NLI models, recent ... |
Title: Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication Abstract: Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 Q... |
Title: Kernel similarity matching with Hebbian neural networks Abstract: Recent works have derived neural networks with online correlation-based learning rules to perform \textit{kernel similarity matching}. These works applied existing linear similarity matching algorithms to nonlinear features generated with random F... |
Title: Towards PAC Multi-Object Detection and Tracking Abstract: Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation. However, it remains challenging to provide guarantees on the performance of state-of-the-art techniques based on deep learning. We... |
Title: Big-means: Less is More for K-means Clustering Abstract: K-means clustering plays a vital role in data mining. However, its performance drastically drops when applied to huge amounts of data. We propose a new heuristic that is built on the basis of regular K-means for faster and more accurate big data clustering... |
Title: A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning Abstract: Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology studen... |
Title: Synthesizing Informative Training Samples with GAN Abstract: Remarkable progress has been achieved in synthesizing photo-realistic images with generative adversarial neural networks (GANs). Recently, GANs are utilized as the training sample generator when obtaining or storing real training data is expensive even... |
Title: Neural Structured Prediction for Inductive Node Classification Abstract: This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. This problem has been extensively studied with graph ... |
Title: Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity Abstract: Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-order moment tensors. Unli... |
Title: Accurate ADMET Prediction with XGBoost Abstract: The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety. In this work, we applied an ensemble of features, including fingerprints and descriptors, and a tree-based mach... |
Title: Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer Abstract: Though deep generative models have gained a lot of attention, most of the existing works are designed for the unimodal generation task. In this paper, we explore a new method for unconditional image-text pair generation. We propos... |
Title: Selecting Continuous Life-Like Cellular Automata for Halting Unpredictability: Evolving for Abiogenesis Abstract: Substantial efforts have been applied to engineer CA with desired emergent properties, such as supporting gliders. Recent work in continuous CA has generated a wide variety of compelling bioreminisce... |
Title: CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection Abstract: Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of dynamic bio-molecules. However, cryo-EM data acqui... |
Title: Efficient Architecture Search for Diverse Tasks Abstract: While neural architecture search (NAS) has enabled automated machine learning (AutoML) for well-researched areas, its application to tasks beyond computer vision is still under-explored. As less-studied domains are precisely those where we expect AutoML t... |
Title: Streaming Align-Refine for Non-autoregressive Deliberation Abstract: We propose a streaming non-autoregressive (non-AR) decoding algorithm to deliberate the hypothesis alignment of a streaming RNN-T model. Our algorithm facilitates a simple greedy decoding procedure, and at the same time is capable of producing ... |
Title: Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection Abstract: Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal transmission technique to improve the spectral efficiency (SE) of future communication systems. However, such improvements of the SE are at the c... |
Title: The Distributed Information Bottleneck reveals the explanatory structure of complex systems Abstract: The fruits of science are relationships made comprehensible, often by way of approximation. While deep learning is an extremely powerful way to find relationships in data, its use in science has been hindered by... |
Title: Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network Abstract: Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generall... |
Title: Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning Abstract: An ideal learned representation should display transferability and robustness. Supervised contrastive learning (SupCon) is a promising method for training accurate models, but produces representations that do not c... |
Title: Sources of Irreproducibility in Machine Learning: A Review Abstract: Lately, several benchmark studies have shown that the state of the art in some of the sub-fields of machine learning actually has not progressed despite progress being reported in the literature. The lack of progress is partly caused by the irr... |
Title: $\Upsilon$-Net: A Spatiospectral Network for Retinal OCT Segmentation Abstract: Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-fr... |
Title: DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning Abstract: We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique impe... |
Title: TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets Abstract: The best neural architecture for a given machine learning problem depends on many factors: not only the complexity and structure of the dataset, but also on resource constraints including latency, compute, energy consumption,... |
Title: Evaluating the Effectiveness of Corrective Demonstrations and a Low-Cost Sensor for Dexterous Manipulation Abstract: Imitation learning is a promising approach to help robots acquire dexterous manipulation capabilities without the need for a carefully-designed reward or a significant computational effort. Howeve... |
Title: A generative neural network model for random dot product graphs Abstract: We present GraphMoE, a novel neural network-based approach to learning generative models for random graphs. The neural network is trained to match the distribution of a class of random graphs by way of a moment estimator. The features used... |
Title: Learning time-dependent PDE solver using Message Passing Graph Neural Networks Abstract: One of the main challenges in solving time-dependent partial differential equations is to develop computationally efficient solvers that are accurate and stable. Here, we introduce a graph neural network approach to finding ... |
Title: Deep Unlearning via Randomized Conditionally Independent Hessians Abstract: Recent legislation has led to interest in machine unlearning, i.e., removing specific training samples from a predictive model as if they never existed in the training dataset. Unlearning may also be required due to corrupted/adversarial... |
Title: Accurate detection of sepsis at ED triage using machine learning with clinical natural language processing Abstract: Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Accurate detection of sepsis during emergency department triage would ... |
Title: It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection Abstract: Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this di... |
Title: Conditional Injective Flows for Bayesian Imaging Abstract: Most deep learning models for computational imaging regress a single reconstructed image. In practice, however, ill-posedness, nonlinearity, model mismatch, and noise often conspire to make such point estimates misleading or insufficient. The Bayesian ap... |
Title: Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations Abstract: Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galaxi... |
Title: Safe Self-Refinement for Transformer-based Domain Adaptation Abstract: Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a large domain gap lies between the source and target domains. I... |
Title: Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners Abstract: Traditional multi-task learning (MTL) methods use dense networks that use the same set of shared weights across several different tasks. This often creates interference where two or more tasks compete to pull model parameters in diffe... |
Title: Theory of Graph Neural Networks: Representation and Learning Abstract: Graph Neural Networks (GNNs), neural network architectures targeted to learning representations of graphs, have become a popular learning model for prediction tasks on nodes, graphs and configurations of points, with wide success in practice.... |
Title: On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression Abstract: We study the acceleration of the Local Polynomial Interpolation-based Gradient Descent method (LPI-GD) recently proposed for the approximate solution of empirical risk minimization problems (ERM). We focus o... |
Title: FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression Abstract: Kernel smooth is the most fundamental technique for data density and regression estimation. However, time-consuming is the biggest obstacle for the application that the direct evaluation of kernel smooth for $N$ samples needs ${O}\left( {{... |
Title: Stress-Testing LiDAR Registration Abstract: Point cloud registration (PCR) is an important task in many fields including autonomous driving with LiDAR sensors. PCR algorithms have improved significantly in recent years, by combining deep-learned features with robust estimation methods. These algorithms succeed i... |
Title: Searching Intrinsic Dimensions of Vision Transformers Abstract: It has been shown by many researchers that transformers perform as well as convolutional neural networks in many computer vision tasks. Meanwhile, the large computational costs of its attention module hinder further studies and applications on edge ... |
Title: Semantic interpretation for convolutional neural networks: What makes a cat a cat? Abstract: The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundamental limitations remain, however, that imp... |
Title: A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis Abstract: Recognizing the type of connected devices to a network helps to perform security policies. In smart grids, identifying massive number of grid metering terminals based on network traffic analysis is almost blank and existing ... |
Title: The Tree Loss: Improving Generalization with Many Classes Abstract: Multi-class classification problems often have many semantically similar classes. For example, 90 of ImageNet's 1000 classes are for different breeds of dog. We should expect that these semantically similar classes will have similar parameter ve... |
Title: Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning Abstract: Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observat... |
Title: Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion Abstract: Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write pe... |
Title: DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup Abstract: Recently, federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. Nevertheless, directly applying federated learnin... |
Title: Tensor-networks for High-order Polynomial Approximation: A Many-body Physics Perspective Abstract: We analyze the problem of high-order polynomial approximation from a many-body physics perspective, and demonstrate the descriptive power of entanglement entropy in capturing model capacity and task complexity. Ins... |
Title: A Variational Approach to Bayesian Phylogenetic Inference Abstract: Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper, we pr... |
Title: Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case Abstract: Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvemen... |
Title: Visual Attention Methods in Deep Learning: An In-Depth Survey Abstract: Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed a... |
Title: Cannikin's Law in Tensor Modeling: A Rank Study for Entanglement and Separability in Tensor Complexity and Model Capacity Abstract: This study clarifies the proper criteria to assess the modeling capacity of a general tensor model. The work analyze the problem based on the study of tensor ranks, which is not a w... |
Title: UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio Abstract: We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-... |
Title: A Distributed and Elastic Aggregation Service for Scalable Federated Learning Systems Abstract: Federated Learning has promised a new approach to resolve the challenges in machine learning by bringing computation to the data. The popularity of the approach has led to rapid progress in the algorithmic aspects and... |
Title: SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment Abstract: In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malwar... |
Title: FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning Abstract: This paper investigates efficient distributed training of a Federated Learning~(FL) model over a wireless network of wireless devices. The communication iterations of the distributed training algorithm may be... |
Title: TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark Abstract: Food Computing is currently a fast-growing field of research. Natural language processing (NLP) is also increasingly essential in this field, especially for recognising food entities. However, there are still only a few well-defined tas... |
Title: Exploiting Multiple EEG Data Domains with Adversarial Learning Abstract: Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly sub... |
Title: Approaching sales forecasting using recurrent neural networks and transformers Abstract: Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, e... |
Title: Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices Abstract: A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform highly accurate representation learning on... |
Title: A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data Abstract: High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method that extracts useful yet latent knowledge... |
Title: Beyond L1: Faster and Better Sparse Models with skglm Abstract: We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent... |
Title: Optimizing differential equations to fit data and predict outcomes Abstract: Many scientific problems focus on observed patterns of change or on how to design a system to achieve particular dynamics. Those problems often require fitting differential equation models to target trajectories. Fitting such models can... |
Title: What If: Generating Code to Answer Simulation Questions Abstract: Many texts, especially in chemistry and biology, describe complex processes. We focus on texts that describe a chemical reaction process and questions that ask about the process's outcome under different environmental conditions. To answer questio... |
Title: nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models Abstract: This paper describes our submission to the Competition on Legal Information Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for tasks 1 and 2. Task 1 is a legal case retri... |
Title: IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G Abstract: In this article, we present our vision of preamble detection in a physical random access channel for next-generation (Next-G) networks using machine learning techniques. Preamble detection is performed to maintain communicati... |
Title: Alternating Channel Estimation and Prediction for Cell-Free mMIMO with Channel Aging: A Deep Learning Based Scheme Abstract: In large scale dynamic wireless networks, the amount of overhead caused by channel estimation (CE) is becoming one of the main performance bottlenecks. This is due to the large number user... |
Title: Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System Abstract: Integration of Machine Learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guide... |
Title: Assessing Differentially Private Variational Autoencoders under Membership Inference Abstract: We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders. Our work complements previous work in two aspects. First, we evaluate the the strong re... |
Title: Polynomial-time Sparse Deconvolution Abstract: How can a probability measure be recovered with sparse support from its generalized moments? This problem, called sparse deconvolution, has been the focus of research in mathematics, theoretical computer science, and neural computing. However, there is no polynomial... |
Title: Accelerated MRI With Deep Linear Convolutional Transform Learning Abstract: Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications. Unlike CS that is typically implemented with pre-deter... |
Title: StyleT2F: Generating Human Faces from Textual Description Using StyleGAN2 Abstract: AI-driven image generation has improved significantly in recent years. Generative adversarial networks (GANs), like StyleGAN, are able to generate high-quality realistic data and have artistic control over the output, as well. In... |
Title: Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning Abstract: While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples. Considering... |
Title: Unsupervised Cross-Task Generalization via Retrieval Augmentation Abstract: Humans can perform unseen tasks by recalling relevant skills that are acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve such cross-task generaliza... |
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