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Title: Factuality Enhanced Language Models for Open-Ended Text Generation Abstract: Pretrained language models (LMs) are susceptible to generate text with nonfactual information. In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation. We design the FactualityPrompts ... |
Title: A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning Abstract: We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loos... |
Title: On the Generalization and Adaption Performance of Causal Models Abstract: Learning models that offer robust out-of-distribution generalization and fast adaptation is a key challenge in modern machine learning. Modelling causal structure into neural networks holds the promise to accomplish robust zero and few-sho... |
Title: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models Abstract: Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly charac... |
Title: Explicit Regularization in Overparametrized Models via Noise Injection Abstract: Injecting noise within gradient descent has several desirable features. In this paper, we explore noise injection before computing a gradient step, which is known to have smoothing and regularizing properties. We show that small per... |
Title: On Margins and Generalisation for Voting Classifiers Abstract: We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes theory. These provide state-of-the-art guarantees on a number of classification tasks. Our cent... |
Title: On Neural Architecture Inductive Biases for Relational Tasks Abstract: Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generalization. This is especially true in the case of tasks involving abstract relations like recognizing rule... |
Title: Field Level Neural Network Emulator for Cosmological N-body Simulations Abstract: We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and velocities of N-b... |
Title: Privacy Leakage in Text Classification: A Data Extraction Approach Abstract: Recent work has demonstrated the successful extraction of training data from generative language models. However, it is not evident whether such extraction is feasible in text classification models since the training objective is to pre... |
Title: Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models Abstract: We establish optimal Statistical Query (SQ) lower bounds for robustly learning certain families of discrete high-dimensional distributions. In particular, we show that no efficient SQ algorithm with access to ... |
Title: Clustering with Queries under Semi-Random Noise Abstract: The seminal paper by Mazumdar and Saha \cite{MS17a} introduced an extensive line of work on clustering with noisy queries. Yet, despite significant progress on the problem, the proposed methods depend crucially on knowing the exact probabilities of errors... |
Title: Transformer based Urdu Handwritten Text Optical Character Reader Abstract: Extracting Handwritten text is one of the most important components of digitizing information and making it available for large scale setting. Handwriting Optical Character Reader (OCR) is a research problem in computer vision and natural... |
Title: Simple lessons from complex learning: what a neural network model learns about cosmic structure formation Abstract: We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green... |
Title: Revisiting End-to-End Speech-to-Text Translation From Scratch Abstract: End-to-end (E2E) speech-to-text translation (ST) often depends on pretraining its encoder and/or decoder using source transcripts via speech recognition or text translation tasks, without which translation performance drops substantially. Ho... |
Title: Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint Abstract: Overparameterized neural networks enjoy great representation power on complex data, and more importantly yield sufficiently smooth output, which is crucial to their generalization and robus... |
Title: A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning Abstract: The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem. Existing methods try to extr... |
Title: Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments Abstract: Learning from demonstration methods usually leverage close to optimal demonstrations to accelerate training. By contrast, when demonstrating a task, human teachers deviate from optimal demonstrations and pedagogically mod... |
Title: ECLAD: Extracting Concepts with Local Aggregated Descriptors Abstract: Convolutional neural networks are being increasingly used in critical systems, where ensuring their robustness and alignment is crucial. In this context, the field of explainable artificial intelligence has proposed the generation of high-lev... |
Title: DORA: Exploring outlier representations in Deep Neural Networks Abstract: Deep Neural Networks (DNNs) draw their power from the representations they learn. In recent years, however, researchers have found that DNNs, while being incredibly effective in learning complex abstractions, also tend to be infected with ... |
Title: An FPGA-based Solution for Convolution Operation Acceleration Abstract: Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computi... |
Title: Accurate Node Feature Estimation with Structured Variational Graph Autoencoder Abstract: Given a graph with partial observations of node features, how can we estimate the missing features accurately? Feature estimation is a crucial problem for analyzing real-world graphs whose features are commonly missing durin... |
Title: What is a Good Metric to Study Generalization of Minimax Learners? Abstract: Minimax optimization has served as the backbone of many machine learning (ML) problems. Although the convergence behavior of optimization algorithms has been extensively studied in the minimax settings, their generalization guarantees i... |
Title: Unlearning Protected User Attributes in Recommendations with Adversarial Training Abstract: Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, race, and location. These encoded biases c... |
Title: Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization Abstract: A number of variational autoencoders (VAEs) have recently emerged with the aim of modeling multimodal data, e.g., to jointly model images and their corresponding captions. Still, multimodal VAEs tend to focus solely on a subset ... |
Title: Redundancy in Deep Linear Neural Networks Abstract: Conventional wisdom states that deep linear neural networks benefit from expressiveness and optimization advantages over a single linear layer. This paper suggests that, in practice, the training process of deep linear fully-connected networks using conventiona... |
Title: Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning Abstract: Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural networks have emerged as a prevalent paradigm of lea... |
Title: Receding Horizon Inverse Reinforcement Learning Abstract: Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizon inverse reinforcement learning (RHIRL), a new IRL algorithm for high-dime... |
Title: Individually Fair Learning with One-Sided Feedback Abstract: We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances. On each round, $k$ instances arrive and receive classification outcomes according to a rando... |
Title: Early Transferability of Adversarial Examples in Deep Neural Networks Abstract: This paper will describe and analyze a new phenomenon that was not known before, which we call "Early Transferability". Its essence is that the adversarial perturbations transfer among different networks even at extremely early stage... |
Title: Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective Abstract: The graph neural network (GNN) has demonstrated its superior performance in various applications. The working mechanism behind it, however, remains mysterious. GNN models are designed to learn effective representations for ... |
Title: Meet You Halfway: Explaining Deep Learning Mysteries Abstract: Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art results. While these models are highly expressive and achieve impressively accurate solutions with excellent generalization abilities, they are susceptibl... |
Title: SDQ: Stochastic Differentiable Quantization with Mixed Precision Abstract: In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used. In addition, as new hardware that supports mixed bitwidth arithmetic operations, recent research on mixed preci... |
Title: Choosing Answers in $\varepsilon$-Best-Answer Identification for Linear Bandits Abstract: In pure-exploration problems, information is gathered sequentially to answer a question on the stochastic environment. While best-arm identification for linear bandits has been extensively studied in recent years, few works... |
Title: Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer Abstract: Although autoregressive models have achieved promising results on image generation, their unidirectional generation process prevents the resultant images from fully reflecting global contexts. To address the issue, we propose a... |
Title: Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks Abstract: Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems. The involved sparsifying transformations or dictionaries are ty... |
Title: Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk Abstract: Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation. Most of the existing methods for safe rei... |
Title: Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems Abstract: This work studies theoretical performance guarantees of a ubiquitous reinforcement learning policy for controlling the canonical model of stochastic linear-quadratic system. We show that randomized certainty e... |
Title: Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes] Abstract: Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional approach uses data to estimat... |
Title: AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing Abstract: $\mathbf{Purpose}$: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness from a single optical... |
Title: Learning to generalize Dispatching rules on the Job Shop Scheduling Abstract: This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this w... |
Title: Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy Abstract: This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel E... |
Title: Unsupervised Learning of the Total Variation Flow Abstract: The total variation (TV) flow generates a scale-space representation of an image based on the TV functional. This gradient flow observes desirable features for images such as sharp edges and enables spectral, scale, and texture analysis. The standard nu... |
Title: Conformal Off-Policy Prediction in Contextual Bandits Abstract: Most off-policy evaluation methods for contextual bandits have focused on the expected outcome of a policy, which is estimated via methods that at best provide only asymptotic guarantees. However, in many applications, the expectation may not be the... |
Title: Depression Recognition using Remote Photoplethysmography from Facial Videos Abstract: Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological si... |
Title: Xplique: A Deep Learning Explainability Toolbox Abstract: Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing th... |
Title: HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask Abstract: Federated learning alleviates the privacy risk in distributed learning by transmitting only the local model updates to the central server. However, it faces challenges including statistical heterogeneity of clients' datasets... |
Title: Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning Abstract: World models in model-based reinforcement learning usually face unrealistic long-time-horizon prediction issues due to compounding errors as the prediction errors accumulate over timesteps. Recent works in graph-struc... |
Title: Concept Identification for Complex Engineering Datasets Abstract: Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful know... |
Title: Uncovering bias in the PlantVillage dataset Abstract: We report our investigation on the use of the popular PlantVillage dataset for training deep learning based plant disease detection models. We trained a machine learning model using only 8 pixels from the PlantVillage image backgrounds. The model achieved 49.... |
Title: Diagnosing Ensemble Few-Shot Classifiers Abstract: The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually difficult to understand the underlying causes and make improvements. To tackle this iss... |
Title: Model Degradation Hinders Deep Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks.However, drastic performance degradation is always observed when a GNN is stacked with many layers. As a result, most GNNs only have shallow architectures, which l... |
Title: A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks Abstract: We study the fundamental limits to the expressive power of neural networks. Given two sets $F$, $G$ of real-valued functions, we first prove a general lower bound on how well functions in $F$ can be app... |
Title: Trajectory-dependent Generalization Bounds for Deep Neural Networks via Fractional Brownian Motion Abstract: Despite being tremendously overparameterized, it is appreciated that deep neural networks trained by stochastic gradient descent (SGD) generalize surprisingly well. Based on the Rademacher complexity of a... |
Title: A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data Abstract: Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the... |
Title: Graph Attention Multi-Layer Perceptron Abstract: Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for large-scale g... |
Title: NNTrainer: Light-Weight On-Device Training Framework Abstract: Modern consumer electronic devices have adopted deep learning-based intelligence services for their key features. Vendors have recently started to execute intelligence services on devices to preserve personal data in devices, reduce network and cloud... |
Title: Learning to generate imaginary tasks for improving generalization in meta-learning Abstract: The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of the assumption in applications with either... |
Title: Audio-video fusion strategies for active speaker detection in meetings Abstract: Meetings are a common activity in professional contexts, and it remains challenging to endow vocal assistants with advanced functionalities to facilitate meeting management. In this context, a task like active speaker detection can ... |
Title: CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization Abstract: For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anoma... |
Title: Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks Abstract: Adversarial examples, which are usually generated for specific inputs with a specific model, are ubiquitous for neural networks. In this paper we unveil a surprising property of adversarial noises when they are put together, ... |
Title: Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms Abstract: The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on the test set, when the instances are preci... |
Title: GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing Abstract: Certified defenses such as randomized smoothing have shown promise towards building reliable machine learning systems against $\ell_p$-norm bounded attacks. However, existing methods are insufficient or ... |
Title: Unveiling Transformers with LEGO: a synthetic reasoning task Abstract: We propose a synthetic task, LEGO (Learning Equality and Group Operations), that encapsulates the problem of following a chain of reasoning, and we study how the transformer architecture learns this task. We pay special attention to data effe... |
Title: OptWedge: Cognitive Optimized Guidance toward Off-screen POIs Abstract: Guiding off-screen points of interest (POIs) is a practical way of providing additional information to users of small-screen devices, such as smart devices and head-mounted displays. Popular previous methods involve displaying a primitive fi... |
Title: Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms Abstract: The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website applic... |
Title: Evaluating Aleatoric Uncertainty via Conditional Generative Models Abstract: Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learning applications. Previous research on aleatoric uncerta... |
Title: Pseudo-Poincaré: A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks Abstract: Hyperbolic neural networks have recently gained significant attention due to their promising results on several graph problems including node classification and link prediction. The primary reason for this succe... |
Title: Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs Abstract: The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter servers with powerful CPUs and GPUs, modern smartphones cons... |
Title: Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information Abstract: In real-world reinforcement learning applications the learner's observation space is ubiquitously high-dimensional with both relevant and irrelevant information about the task at hand. Learning from high-dimensional observ... |
Title: Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis Abstract: Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i... |
Title: On Transfer Learning in Functional Linear Regression Abstract: This work studies the problem of transfer learning under the functional linear model framework, which aims to improve the fit of the target model by leveraging the knowledge from related source models. We measure the relatedness between target and so... |
Title: Robust Matrix Completion with Heavy-tailed Noise Abstract: This paper studies low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has a... |
Title: A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis Abstract: The Strong Lottery Ticket Hypothesis (SLTH) stipulates the existence of a subnetwork within a sufficiently overparameterized (dense) neural network that -- when initialized randomly and without any training -- achieves the... |
Title: Unsupervised Deep Discriminant Analysis Based Clustering Abstract: This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and maximize the inter-cluster discrepancy in an unsupervised manner. The... |
Title: There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes Abstract: Interpretability is an essential building block for trustworthiness in reinforcement learning systems. However, interpretability might come at the cost of deteriorated performance, leading many researchers to build compl... |
Title: Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference Abstract: By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or continue the next layer... |
Title: ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning Abstract: What target labels are most effective for graph neural network (GNN) training? In some applications where GNNs excel-like drug design or fraud detection, labeling new instances is expensive. We develop a data-eff... |
Title: Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI Abstract: Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging data has significant implications, as extracting useful information from neuroimaging data for ASD detection is challenging. Even though machine ... |
Title: An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems Abstract: Unit commitment (UC) is a fundamental problem in the day-ahead electricity market, and it is critical to solve UC problems efficiently. Mathematical optimization techniques like dynamic prog... |
Title: Improved Approximation for Fair Correlation Clustering Abstract: Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge. Motivated by this, we study Fair Correlation Clustering where the data points may belong to different protected group... |
Title: Enhancement of Healthcare Data Transmission using the Levenberg-Marquardt Algorithm Abstract: In the healthcare system, patients are required to use wearable devices for the remote data collection and real-time monitoring of health data and the status of health conditions. This adoption of wearables results in a... |
Title: Analytical Composition of Differential Privacy via the Edgeworth Accountant Abstract: Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition. In this study, we introduce the Ed... |
Title: Temporal Inductive Logic Reasoning Abstract: Inductive logic reasoning is one of the fundamental tasks on graphs, which seeks to generalize patterns from the data. This task has been studied extensively for traditional graph datasets such as knowledge graphs (KGs), with representative techniques such as inductiv... |
Title: GCVAE: Generalized-Controllable Variational AutoEncoder Abstract: Variational autoencoders (VAEs) have recently been used for unsupervised disentanglement learning of complex density distributions. Numerous variants exist to encourage disentanglement in latent space while improving reconstruction. However, none ... |
Title: Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction Abstract: Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods in various tasks such as node class... |
Title: It's a super deal -- train recurrent network on noisy data and get smooth prediction free Abstract: Recent research demonstrate that prediction of time series by predictive recurrent neural networks based on the noisy input generates a {\it smooth} anticipated trajectory. We examine influence of the noise compon... |
Title: Deep Surrogate Assisted Generation of Environments Abstract: Recent progress in reinforcement learning (RL) has started producing generally capable agents that can solve a distribution of complex environments. These agents are typically tested on fixed, human-authored environments. On the other hand, quality div... |
Title: Exploring Predictive States via Cantor Embeddings and Wasserstein Distance Abstract: Predictive states for stochastic processes are a nonparametric and interpretable construct with relevance across a multitude of modeling paradigms. Recent progress on the self-supervised reconstruction of predictive states from ... |
Title: ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion Abstract: Knowledge graphs are inherently incomplete. Therefore substantial research has been directed towards knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). ... |
Title: CCP: Correlated Clustering and Projection for Dimensionality Reduction Abstract: Most dimensionality reduction methods employ frequency domain representations obtained from matrix diagonalization and may not be efficient for large datasets with relatively high intrinsic dimensions. To address this challenge, Cor... |
Title: RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation Abstract: Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions. To achieve fast and accurate visual assis... |
Title: Reinforced Inverse Scattering Abstract: Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves. In order to collect information, sensors are put in different locations to send and receive waves from each other. The choice of sensor positions a... |
Title: Learning in Distributed Contextual Linear Bandits Without Sharing the Context Abstract: Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wireless where communication constraints can be a ... |
Title: VN-Transformer: Rotation-Equivariant Attention for Vector Neurons Abstract: Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations. ... |
Title: On Gradient Descent Convergence beyond the Edge of Stability Abstract: Gradient Descent (GD) is a powerful workhorse of modern machine learning thanks to its scalability and efficiency in high-dimensional spaces. Its ability to find local minimisers is only guaranteed for losses with Lipschitz gradients, where i... |
Title: CASS: Cross Architectural Self-Supervision for Medical Image Analysis Abstract: Recent advances in Deep Learning and Computer Vision have alleviated many of the bottlenecks, allowing algorithms to be label-free with better performance. Specifically, Transformers provide a global perspective of the image, which C... |
Title: Alternating Mirror Descent for Constrained Min-Max Games Abstract: In this paper we study two-player bilinear zero-sum games with constrained strategy spaces. An instance of natural occurrences of such constraints is when mixed strategies are used, which correspond to a probability simplex constraint. We propose... |
Title: Ensembling Framework for Texture Extraction Techniques for Classification Abstract: In the past few years, texture-based classification problems have proven their significance in many domains, from industrial inspection to health-related applications. New techniques and CNN-based architectures have been develope... |
Title: A Comprehensive Survey of Graph-based Deep Learning Approaches for Anomaly Detection in Complex Distributed Systems Abstract: Anomaly detection is an important problem for complex distributed systems consisting of hardware and software components. A thorough understanding of the requirements and challenges of an... |
Title: TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression Abstract: The tree-based ensembles are known for their outstanding performance for classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering ... |
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