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Title: Object discovery and representation networks Abstract: The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks. While there has been excellent progress with simple, image-level learning, recent methods have shown the advantage of including knowledge of ... |
Title: Are Shortest Rationales the Best Explanations for Human Understanding? Abstract: Existing self-explaining models typically favor extracting the shortest possible rationales - snippets of an input text "responsible for" corresponding output - to explain the model prediction, with the assumption that shorter ratio... |
Title: Zero Pixel Directional Boundary by Vector Transform Abstract: Boundaries are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the label representation, which typically leads to class imbalance and, as a consequence, to thick boundaries that... |
Title: A Continual Learning Framework for Adaptive Defect Classification and Inspection Abstract: Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes. This article describes a general framework for classifying defects from high volum... |
Title: Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity Abstract: Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it has been observed that PINNs are difficult to ... |
Title: New directions for surrogate models and differentiable programming for High Energy Physics detector simulation Abstract: The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas o... |
Title: Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set Abstract: Access to dermatological care is a major issue, with an estimated 3 billion people lacking access to care globally. Artificial intelligence (AI) may aid in triaging skin diseases. However, most AI models have not been ri... |
Title: Neural-Network-Directed Genetic Programmer for Discovery of Governing Equations Abstract: We develop a symbolic regression framework for extracting the governing mathematical expressions from observed data. The evolutionary approach, faiGP, is designed to leverage the properties of a function algebra that have b... |
Title: Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling Abstract: Speech Emotion Recognition (SER) application is frequently associated with privacy concerns as it often acquires and transmits speech data at the client-side to remote cloud platfo... |
Title: Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography Abstract: A major limitation in applying deep learning to artificial intelligence (AI) systems is the scarcity of high-quality curated datasets. We investigate strong augmentation based self-supervised learning (SSL) techni... |
Title: Example Perplexity Abstract: Some examples are easier for humans to classify than others. The same should be true for deep neural networks (DNNs). We use the term example perplexity to refer to the level of difficulty of classifying an example. In this paper, we propose a method to measure the perplexity of an e... |
Title: QUBOs for Sorting Lists and Building Trees Abstract: We show that the fundamental tasks of sorting lists and building search trees or heaps can be modeled as quadratic unconstrained binary optimization problems (QUBOs). The idea is to understand these tasks as permutation problems and to devise QUBOs whose solut... |
Title: Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables Abstract: World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data colle... |
Title: DePS: An improved deep learning model for de novo peptide sequencing Abstract: De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetativ... |
Title: Understanding robustness and generalization of artificial neural networks through Fourier masks Abstract: Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain... |
Title: Discovering the building blocks of dark matter halo density profiles with neural networks Abstract: The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network model that is trained to learn the m... |
Title: Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks Abstract: We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time mode... |
Title: Noisy Tensor Completion via Low-rank Tensor Ring Abstract: Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-fre... |
Title: SC2: Supervised Compression for Split Computing Abstract: Split computing distributes the execution of a neural network (e.g., for a classification task) between a mobile device and a more powerful edge server. A simple alternative to splitting the network is to carry out the supervised task purely on the edge s... |
Title: Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation Abstract: Uncertainty estimation in deep learning has become a leading research field in medical image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Most approaches for uncertainty estim... |
Title: On Redundancy and Diversity in Cell-based Neural Architecture Search Abstract: Searching for the architecture cells is a dominant paradigm in NAS. However, little attention has been devoted to the analysis of the cell-based search spaces even though it is highly important for the continual development of NAS. In... |
Title: The Mathematics of Artificial Intelligence Abstract: We currently witness the spectacular success of artificial intelligence in both science and public life. However, the development of a rigorous mathematical foundation is still at an early stage. In this survey article, which is based on an invited lecture at ... |
Title: Multimodal Learning on Graphs for Disease Relation Extraction Abstract: Objective: Disease knowledge graphs are a way to connect, organize, and access disparate information about diseases with numerous benefits for artificial intelligence (AI). To create knowledge graphs, it is necessary to extract knowledge fro... |
Title: Neural network processing of holographic images Abstract: HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles, such as water droplets and ice crystals. Cloud particle properties include position, diameter, and s... |
Title: Adversarial Support Alignment Abstract: We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matched. We propose symmetric support difference as a divergence measure to quantify the mismatch... |
Title: Memorizing Transformers Abstract: Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this... |
Title: Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing Abstract: The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably... |
Title: $\ell_p$ Slack Norm Support Vector Data Description Abstract: The support vector data description (SVDD) approach serves as a de facto standard for one-class classification where the learning task entails inferring the smallest hyper-sphere to enclose target objects while linearly penalising any errors/slacks vi... |
Title: Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning Abstract: We introduce Backpropagation Through Time and Space (BPTTS), a method for training a recurrent spatio-temporal neural network, that is used in a homogeneous multi-agent reinforcement learning (MAR... |
Title: Provable Adversarial Robustness for Fractional Lp Threat Models Abstract: In recent years, researchers have extensively studied adversarial robustness in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm bounded adversarial attacks. However, attacks bounded by fractional L_p "norms" (quasi... |
Title: Latent-Variable Advantage-Weighted Policy Optimization for Offline RL Abstract: Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting is particularly well-suited for continuous control r... |
Title: Meta-Learning of NAS for Few-shot Learning in Medical Image Applications Abstract: Deep learning methods have been successful in solving tasks in machine learning and have made breakthroughs in many sectors owing to their ability to automatically extract features from unstructured data. However, their performanc... |
Title: Risk-Averse No-Regret Learning in Online Convex Games Abstract: We consider an online stochastic game with risk-averse agents whose goal is to learn optimal decisions that minimize the risk of incurring significantly high costs. Specifically, we use the Conditional Value at Risk (CVaR) as a risk measure that the... |
Title: On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers Abstract: Every uncalibrated classifier has a corresponding true calibration map that calibrates its confidence. Deviations of this idealistic map from the identity map reveal miscalibration. Such calibration errors can be red... |
Title: Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training Abstract: In this paper, we introduce a novel neural network training framework that increases model's adversarial robustness to adversarial attacks while maintaining high clean accuracy by combining contrastive learning (CL... |
Title: On the Convergence of Certified Robust Training with Interval Bound Propagation Abstract: Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of ... |
Title: 3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation Abstract: Medical image segmentation has been so far achieving promising results with Convolutional Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer tends to discard important information such as positions. Mo... |
Title: A Survey of Multi-Agent Reinforcement Learning with Communication Abstract: Communication is an effective mechanism for coordinating the behavior of multiple agents. In the field of multi-agent reinforcement learning, agents can improve the overall learning performance and achieve their objectives by communicati... |
Title: Adaptive n-ary Activation Functions for Probabilistic Boolean Logic Abstract: Balancing model complexity against the information contained in observed data is the central challenge to learning. In order for complexity-efficient models to exist and be discoverable in high dimensions, we require a computational fr... |
Title: AI Autonomy: Self-Initiation, Adaptation and Continual Learning Abstract: As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retra... |
Title: Kan Extensions in Data Science and Machine Learning Abstract: A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a po... |
Title: Graph Augmentation Learning Abstract: Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the u... |
Title: Phased Flight Trajectory Prediction with Deep Learning Abstract: The unprecedented increase of commercial airlines and private jets over the next ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contribut... |
Title: GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis Abstract: In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs... |
Title: HybridNets: End-to-End Perception Network Abstract: End-to-end Network has become increasingly important in multi-tasking. One prominent example of this is the growing significance of a driving perception system in autonomous driving. This paper systematically studies an end-to-end perception network for multi-t... |
Title: Convert, compress, correct: Three steps toward communication-efficient DNN training Abstract: In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training. $\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses ... |
Title: Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning Abstract: This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought... |
Title: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? Abstract: Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class. A recent study has shown a phenomenon called neural collapse that the... |
Title: Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation Abstract: Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention. However, due to their complex architectures and large numbers of parameters, measuring the... |
Title: DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications Abstract: The ability to predict the future trajectory of a patient is a key step toward the development of therapeutics for complex diseases such as Alzheimer's disease (AD). However, most machine learni... |
Title: TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding Abstract: Speaker embedding is an important front-end module to explore discriminative speaker features for many speech applications where speaker information is needed. Current SOTA backbone networks for speaker embedding are designed to aggregat... |
Title: MotionAug: Augmentation with Physical Correction for Human Motion Prediction Abstract: This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. This motion synthesis consists of our modified Variational AutoEn... |
Title: Time and the Value of Data Abstract: Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of keeping ar... |
Title: Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input Abstract: The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize ... |
Title: Are Vision Transformers Robust to Spurious Correlations? Abstract: Deep neural networks may be susceptible to learning spurious correlations that hold on average but not in atypical test samples. As with the recent emergence of vision transformer (ViT) models, it remains underexplored how spurious correlations a... |
Title: Time Dependency, Data Flow, and Competitive Advantage Abstract: Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (busine... |
Title: Contrastive Learning with Positive-Negative Frame Mask for Music Representation Abstract: Self-supervised learning, especially contrastive learning, has made an outstanding contribution to the development of many deep learning research fields. Recently, researchers in the acoustic signal processing field noticed... |
Title: Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning Abstract: Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, t... |
Title: Graph Representation Learning with Individualization and Refinement Abstract: Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data. GNNs follow an approach of message passing analogous to 1-dimensional Weisfeiler Lehman (1-WL) test for graph isomorphi... |
Title: Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers Abstract: Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction... |
Title: Prediction of speech intelligibility with DNN-based performance measures Abstract: This paper presents a speech intelligibility model based on automatic speech recognition (ASR), combining phoneme probabilities from deep neural networks (DNN) and a performance measure that estimates the word error rate from thes... |
Title: Optimal Rejection Function Meets Character Recognition Tasks Abstract: In this paper, we propose an optimal rejection method for rejecting ambiguous samples by a rejection function. This rejection function is trained together with a classification function under the framework of Learning-with-Rejection (LwR). Th... |
Title: How Many Data Samples is an Additional Instruction Worth? Abstract: Recently introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. Instruction-tuned models have significantly outperformed multitask learning models (without instruction); ho... |
Title: On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks Abstract: Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic ... |
Title: Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study Abstract: This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The cells we compare include classical long short term mem... |
Title: Nearest Neighbor Classifier with Margin Penalty for Active Learning Abstract: As deep learning becomes the mainstream in the field of natural language processing, the need for suitable active learning method are becoming unprecedented urgent. Active Learning (AL) methods based on nearest neighbor classifier are ... |
Title: Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding Abstract: Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep learning methods are based on self-... |
Title: Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed Abstract: Gaussian process regression underpins countless academic and industrial applications of machine learning and statistics, with maximum likelihood estimation routinely used to select appropriate parameters for the covariance kernel... |
Title: An Interactive Explanatory AI System for Industrial Quality Control Abstract: Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisio... |
Title: Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data Abstract: The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicl... |
Title: SoK: Differential Privacy on Graph-Structured Data Abstract: In this work, we study the applications of differential privacy (DP) in the context of graph-structured data. We discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph... |
Title: On the Properties of Adversarially-Trained CNNs Abstract: Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning the effectivenes... |
Title: Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning Abstract: Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance.... |
Title: Symmetry-Based Representations for Artificial and Biological General Intelligence Abstract: Biological intelligence is remarkable in its ability to produce complex behaviour in many diverse situations through data efficient, generalisable and transferable skill acquisition. It is believed that learning "good" se... |
Title: Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs Abstract: In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an $\epsilon$-optimal policy with probability $1-\delta$. While minimax optimal algorithms exist for this problem, its instance-d... |
Title: Visualizing Riemannian data with Rie-SNE Abstract: Faithful visualizations of data residing on manifolds must take the underlying geometry into account when producing a flat planar view of the data. In this paper, we extend the classic stochastic neighbor embedding (SNE) algorithm to data on general Riemannian m... |
Title: On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels Abstract: We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels. We prove that, with normalized multi-channel input and ReLU act... |
Title: Explainability in Graph Neural Networks: An Experimental Survey Abstract: Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains. However, similar to all other neural networks models, GNNs suffer from the black-box problem as people cannot un... |
Title: Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI Abstract: We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual... |
Title: Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series Abstract: The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing high quality representations could... |
Title: Stochastic and Private Nonconvex Outlier-Robust PCA Abstract: We develop theoretically guaranteed stochastic methods for outlier-robust PCA. Outlier-robust PCA seeks an underlying low-dimensional linear subspace from a dataset that is corrupted with outliers. We are able to show that our methods, which involve s... |
Title: Transfer learning for cross-modal demand prediction of bike-share and public transit Abstract: The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as o... |
Title: Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics Abstract: As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models ... |
Title: PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks Abstract: Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs that contain t... |
Title: PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer Abstract: Nowadays, our mobility systems are evolving into the era of intelligent vehicles that aim to improve road safety. Due to their vulnerability, pedestrians are the users who will benefit the most from these developments. However,... |
Title: One-Shot Adaptation of GAN in Just One CLIP Abstract: There are many recent research efforts to fine-tune a pre-trained generator with a few target images to generate images of a novel domain. Unfortunately, these methods often suffer from overfitting or under-fitting when fine-tuned with a single target image. ... |
Title: Few-Shot Learning on Graphs Abstract: Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling ... |
Title: Localizing Visual Sounds the Easy Way Abstract: Unsupervised audio-visual source localization aims at localizing visible sound sources in a video without relying on ground-truth localization for training. Previous works often seek high audio-visual similarities for likely positive (sounding) regions and low simi... |
Title: CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation Abstract: Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly u... |
Title: Error estimates for physics informed neural networks approximating the Navier-Stokes equations Abstract: We prove rigorous bounds on the errors resulting from the approximation of the incompressible Navier-Stokes equations with (extended) physics informed neural networks. We show that the underlying PDE residual... |
Title: Dimensionality Reduction and Wasserstein Stability for Kernel Regression Abstract: In a high-dimensional regression framework, we study consequences of the naive two-step procedure where first the dimension of the input variables is reduced and second, the reduced input variables are used to predict the output v... |
Title: Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models Abstract: Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awareness for the an... |
Title: Semi-Markov Offline Reinforcement Learning for Healthcare Abstract: Reinforcement learning (RL) tasks are typically framed as Markov Decision Processes (MDPs), assuming that decisions are made at fixed time intervals. However, many applications of great importance, including healthcare, do not satisfy this assum... |
Title: Gaussian initializations help deep variational quantum circuits escape from the barren plateau Abstract: Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the ex... |
Title: Euler State Networks Abstract: Inspired by the numerical solution of ordinary differential equations, in this paper we propose a novel Reservoir Computing (RC) model, called the Euler State Network (EuSN). The introduced approach makes use of forward Euler discretization and antisymmetric recurrent matrices to d... |
Title: When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation Abstract: Data Augmentation (DA) is known to improve the generalizability of deep neural networks. Most existing DA techniques naively add a certain number of augmented samples without considering the qual... |
Title: A Framework and Benchmark for Deep Batch Active Learning for Regression Abstract: We study the performance of different pool-based Batch Mode Deep Active Learning (BMDAL) methods for regression on tabular data, focusing on methods that do not require to modify the network architecture and training. Our contribut... |
Title: Stability and Risk Bounds of Iterative Hard Thresholding Abstract: In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compres... |
Title: A Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusion Problems Abstract: We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel variants of stochastic Halpern ... |
Title: An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival Abstract: To compare alternative taxi schedules and to compute them, as well as to provide insights into an upcoming taxi trip to drivers and passengers, the duration of a trip or its Estimated Time of Arrival (ETA) is pred... |
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