text stringlengths 0 4.09k |
|---|
Title: RENs: Relevance Encoding Networks Abstract: The manifold assumption for high-dimensional data assumes that the data is generated by varying a set of parameters obtained from a low-dimensional latent space. Deep generative models (DGMs) are widely used to learn data representations in an unsupervised way. DGMs pa... |
Title: Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection Abstract: On-Shelf Availability (OSA) of products in retail stores is a critical business criterion in the fast moving consumer goods and retails sector. When a product is out-of-stock (OOS) and a customer cannot find... |
Title: Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions Abstract: Due to the drastic gap in complexity between sequential and batch statistical learning, recent work has studied a smoothed sequential learning setting, where Nature is constrained to select contexts with density bounde... |
Title: Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking Abstract: Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based... |
Title: Online Deep Equilibrium Learning for Regularization by Denoising Abstract: Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image priors. While ... |
Title: Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments Abstract: We initiate the study of dynamic regret minimization for goal-oriented reinforcement learning modeled by a non-stationary stochastic shortest path problem with changing cost and transition functions. We start by establishi... |
Title: Improving Subgraph Representation Learning via Multi-View Augmentation Abstract: Subgraph representation learning based on Graph Neural Network (GNN) has broad applications in chemistry and biology, such as molecule property prediction and gene collaborative function prediction. On the other hand, graph augmenta... |
Title: EvoVGM: A Deep Variational Generative Model for Evolutionary Parameter Estimation Abstract: Most evolutionary-oriented deep generative models do not explicitly consider the underlying evolutionary dynamics of biological sequences as it is performed within the Bayesian phylogenetic inference framework. In this st... |
Title: Topological Simplification of Signals for Inference and Approximate Reconstruction Abstract: As Internet of Things (IoT) devices become both cheaper and more powerful, researchers are increasingly finding solutions to their scientific curiosities both financially and computationally feasible. When operating with... |
Title: Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification Abstract: Deep Neural Networks (DNN's) are a widely-used solution for a variety of machine learning problems. However, it is often necessary to invest a significant amount of a data scientist's time to pre-process input data, test diff... |
Title: Formalizing Preferences Over Runtime Distributions Abstract: When trying to solve a computational problem we are often faced with a choice among algorithms that are all guaranteed to return the right answer but that differ in their runtime distributions (e.g., SAT solvers, sorting algorithms). This paper aims to... |
Title: Preference Dynamics Under Personalized Recommendations Abstract: Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that individuals' pr... |
Title: People counting system for retail analytics using edge AI Abstract: Developments in IoT applications are playing an important role in our day-to-day life, starting from business predictions to self driving cars. One of the area, most influenced by the field of AI and IoT is retail analytics. In Retail Analytics,... |
Title: BiT: Robustly Binarized Multi-distilled Transformer Abstract: Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine learning, but have also grown in parameters and computational complexity, making them increasingly difficult to deploy in resource-constrained environments. Binariza... |
Title: TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series Abstract: Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the... |
Title: QGNN: Value Function Factorisation with Graph Neural Networks Abstract: In multi-agent reinforcement learning, the use of a global objective is a powerful tool for incentivising cooperation. Unfortunately, it is not sample-efficient to train individual agents with a global reward, because it does not necessarily... |
Title: Inception Transformer Abstract: Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose Inception Transformer, or iFor... |
Title: Federated Adaptation of Reservoirs via Intrinsic Plasticity Abstract: We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario. In particular, our proposal focuses on the adaptation of reservoirs by combining Intrinsic Plasticity with Federated Av... |
Title: Learning Mean Field Games: A Survey Abstract: Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases. Introduced by Lasry and Lions, and Huang, Caines and Malham\'e, Mean Field Games (MFGs) rely on a m... |
Title: Conformal Prediction Intervals with Temporal Dependence Abstract: Cross-sectional prediction is common in many domains such as healthcare, including forecasting tasks using electronic health records, where different patients form a cross-section. We focus on the task of constructing valid prediction intervals (P... |
Title: Predicting Corporate Risk by Jointly Modeling Company Networks and Dialogues in Earnings Conference Calls Abstract: More and more researchers focus on studying company risk prediction based on earnings conference calls because of their free form and rich information. However, existing research does not take spea... |
Title: Mitigating multiple descents: A model-agnostic framework for risk monotonization Abstract: Recent empirical and theoretical analyses of several commonly used prediction procedures reveal a peculiar risk behavior in high dimensions, referred to as double/multiple descent, in which the asymptotic risk is a non-mon... |
Title: Amortized Inference for Causal Structure Learning Abstract: Learning causal structure poses a combinatorial search problem that typically involves evaluating structures using a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is diffi... |
Title: Analytics of Business Time Series Using Machine Learning and Bayesian Inference Abstract: In the survey we consider the case studies on sales time series forecasting, the deep learning approach for forecasting non-stationary time series using time trend correction, dynamic price and supply optimization using Q-l... |
Title: A Neural Tangent Kernel Formula for Ensembles of Soft Trees with Arbitrary Architectures Abstract: A soft tree is an actively studied variant of a decision tree that updates splitting rules using the gradient method. Although it can have various tree architectures, the theoretical properties of their impact are ... |
Title: RADNet: Ensemble Model for Robust Glaucoma Classification in Color Fundus Images Abstract: Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that pathology diagnostics is carried out when the one's sight has already sig... |
Title: Differentially Private Data Generation Needs Better Features Abstract: Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of privacy is simply too large. We advocate instead building off... |
Title: Robust Reinforcement Learning on Graphs for Logistics optimization Abstract: Logistics optimization nowadays is becoming one of the hottest areas in the AI community. In the past year, significant advancements in the domain were achieved by representing the problem in a form of graph. Another promising area of r... |
Title: Trust-based Consensus in Multi-Agent Reinforcement Learning Systems Abstract: An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can prevent a system from accomplishing its intended tasks.... |
Title: Understanding Programmatic Weak Supervision via Source-aware Influence Function Abstract: Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model. With its increasing popularity, it is cr... |
Title: Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images Abstract: We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U... |
Title: Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions Abstract: We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property which holds in a wide range of applications in machine learning and signal processing.... |
Title: A Universal Error Measure for Input Predictions Applied to Online Graph Problems Abstract: We introduce a novel measure for quantifying the error in input predictions. The error is based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a general template which we apply to online gr... |
Title: Removing the fat from your posterior samples with margarine Abstract: Bayesian workflows often require the introduction of nuisance parameters, yet for core science modelling one needs access to a marginal posterior density. In this work we use masked autoregressive flows and kernel density estimators to encapsu... |
Title: Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently Abstract: Driven by the empirical success and wide use of deep neural networks, understanding the generalization performance of overparameterized models has become an increasingly popular question. To this end, there has been substant... |
Title: Gradient-based explanations for Gaussian Process regression and classification models Abstract: Gaussian Processes (GPs) have proven themselves as a reliable and effective method in probabilistic Machine Learning. Thanks to recent and current advances, modeling complex data with GPs is becoming more and more fea... |
Title: Towards Green AI with tensor networks -- Sustainability and innovation enabled by efficient algorithms Abstract: The current standard to compare the performance of AI algorithms is mainly based on one criterion: the model's accuracy. In this context, algorithms with a higher accuracy (or similar measures) are co... |
Title: Impartial Games: A Challenge for Reinforcement Learning Abstract: The AlphaZero algorithm and its successor MuZero have revolutionised several competitive strategy games, including chess, Go, and shogi and video games like Atari, by learning to play these games better than any human and any specialised computer ... |
Title: TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature Abstract: Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural ne... |
Title: Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors Abstract: Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and ... |
Title: Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification Abstract: Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering c... |
Title: An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation Abstract: Gradient descent or its variants are popular in training neural networks. However, in deep Q-learning with neural network approximation, a type of reinforcement learning, gradient descent (als... |
Title: Towards Symbolic Time Series Representation Improved by Kernel Density Estimators Abstract: This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern ... |
Title: An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems Abstract: Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ... |
Title: An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation Abstract: The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the robustness to... |
Title: NECA: Network-Embedded Deep Representation Learning for Categorical Data Abstract: We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attrib... |
Title: Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization Abstract: We consider the problem of minimizing the sum of two convex functions. One of those functions has Lipschitz-continuous gradients, and can be accessed via stochastic oracles, whereas the other is "simpl... |
Title: Machine learning method for return direction forecasting of Exchange Traded Funds using classification and regression models Abstract: This article aims to propose and apply a machine learning method to analyze the direction of returns from Exchange Traded Funds (ETFs) using the historical return data of its com... |
Title: Deep interpretable ensembles Abstract: Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently, partially interpretable semi-structured deep transfor... |
Title: Service Discovery in Social Internet of Things using Graph Neural Networks Abstract: Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering Io... |
Title: VeriFi: Towards Verifiable Federated Unlearning Abstract: Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving par... |
Title: Scalable Online Change Detection for High-dimensional Data Streams Abstract: Detecting changes in data streams is a core objective in their analysis and has applications in, say, predictive maintenance, fraud detection, and medicine. A principled approach to detect changes is to compare distributions observed wi... |
Title: Eliciting Transferability in Multi-task Learning with Task-level Mixture-of-Experts Abstract: Recent work suggests that transformer models are capable of multi-task learning on diverse NLP tasks. However, the potential of these models may be limited as they use the same set of parameters for all tasks. In contra... |
Title: Surprises in adversarially-trained linear regression Abstract: State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is one of the most effective approaches to defend against such examples. We show that for linear reg... |
Title: Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models Abstract: Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP. Pr... |
Title: Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations Abstract: Despite recent explosion in research interests, in-context learning and the precise impact of the quality of demonstrations remain elusive. While, based on current literature, it is expected that in-context learning shares a simi... |
Title: Rethinking Fano's Inequality in Ensemble Learning Abstract: We propose a fundamental theory on ensemble learning that evaluates a given ensemble system by a well-grounded set of metrics. Previous studies used a variant of Fano's inequality of information theory and derived a lower bound of the classification err... |
Title: Training Language Models with Memory Augmentation Abstract: Recent work has improved language models remarkably by equipping them with a non-parametric memory component. However, most existing approaches only introduce memories at testing time, or represent them using a separately trained encoder -- resulting in... |
Title: MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge Abstract: Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient convolutional neural networks for embedded vision ... |
Title: Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization Abstract: We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph tha... |
Title: On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity Abstract: Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite... |
Title: Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification Abstract: The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever b... |
Title: Autoformalization with Large Language Models Abstract: Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial inte... |
Title: Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes Abstract: Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80\% of patients having a 10-year survival period. Given the serious i... |
Title: Mutual Information Divergence: A Unified Metric for Multimodal Generative Models Abstract: Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation,... |
Title: Learning Distributions by Generative Adversarial Networks: Approximation and Generalization Abstract: We study how well generative adversarial networks (GAN) learn probability distributions from finite samples by analyzing the convergence rates of these models. Our analysis is based on a new oracle inequality th... |
Title: ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data Abstract: Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting. However, it remains unclear from where the model learns the task-spe... |
Title: RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning Abstract: Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language. While the progress is promising, it is currently unclear if th... |
Title: Heterogeneous Reservoir Computing Models for Persian Speech Recognition Abstract: Over the last decade, deep-learning methods have been gradually incorporated into conventional automatic speech recognition (ASR) frameworks to create acoustic, pronunciation, and language models. Although it led to significant imp... |
Title: Scalable Multi-Agent Model-Based Reinforcement Learning Abstract: Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a dominant approach for both cooperative and mixed environments due to its cap... |
Title: Towards a Fair Comparison and Realistic Design and Evaluation Framework of Android Malware Detectors Abstract: As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feat... |
Title: Learning dynamics from partial observations with structured neural ODEs Abstract: Identifying dynamical systems from experimental data is a notably difficult task. Prior knowledge generally helps, but the extent of this knowledge varies with the application, and customized models are often needed. We propose a f... |
Title: Learning from time-dependent streaming data with online stochastic algorithms Abstract: We study stochastic algorithms in a streaming framework, trained on samples coming from a dependent data source. In this streaming framework, we analyze the convergence of Stochastic Gradient (SG) methods in a non-asymptotic ... |
Title: RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning Abstract: Prompting has shown impressive success in enabling large pretrained language models (LMs) to perform diverse NLP tasks, especially when only few downstream data are available. Automatically finding the optimal prompt for each task, ... |
Title: Misleading Deep-Fake Detection with GAN Fingerprints Abstract: Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts fro... |
Title: Is a Question Decomposition Unit All We Need? Abstract: Large Language Models (LMs) have achieved state-of-the-art performance on many Natural Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we build bigger and more complex LMs. However, building new LMs may not be an ideal optio... |
Title: A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning Abstract: We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain of probabilistic modeling from Eu... |
Title: Structured Uncertainty in the Observation Space of Variational Autoencoders Abstract: Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and a wide range of applications. Improvements upon the standard VAE mostly focus on the modelling of the posterior distribution o... |
Title: Skill Machines: Temporal Logic Composition in Reinforcement Learning Abstract: A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common approach is to specify tasks through reward machines -- finite state machines that encode the task to be... |
Title: Toward Discovering Options that Achieve Faster Planning Abstract: We propose a new objective for option discovery that emphasizes the computational advantage of using options in planning. For a given set of episodic tasks and a given number of options, the objective prefers options that can be used to achieve a ... |
Title: Exact Phase Transitions in Deep Learning Abstract: This work reports deep-learning-unique first-order and second-order phase transitions, whose phenomenology closely follows that in statistical physics. In particular, we prove that the competition between prediction error and model complexity in the training los... |
Title: Memorization in NLP Fine-tuning Methods Abstract: Large language models are shown to present privacy risks through memorization of training data, and several recent works have studied such risks for the pre-training phase. Little attention, however, has been given to the fine-tuning phase and it is not well unde... |
Title: The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training Abstract: Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or levera... |
Title: Federated Self-supervised Learning for Heterogeneous Clients Abstract: Federated Learning has become an important learning paradigm due to its privacy and computational benefits. As the field advances, two key challenges that still remain to be addressed are: (1) system heterogeneity - variability in the compute... |
Title: SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction Abstract: Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good repr... |
Title: A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers Abstract: The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Despite a lot of empirical studies and recent progress in theoretical ... |
Title: FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation Abstract: Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks construct... |
Title: sat2pc: Estimating Point Cloud of Building Roofs from 2D Satellite Images Abstract: Three-dimensional (3D) urban models have gained interest because of their applications in many use-cases such as urban planning and virtual reality. However, generating these 3D representations requires LiDAR data, which are not ... |
Title: Augmentation-induced Consistency Regularization for Classification Abstract: Deep neural networks have become popular in many supervised learning tasks, but they may suffer from overfitting when the training dataset is limited. To mitigate this, many researchers use data augmentation, which is a widely used and ... |
Title: Linear Algorithms for Nonparametric Multiclass Probability Estimation Abstract: Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recent... |
Title: Investigating Information Inconsistency in Multilingual Open-Domain Question Answering Abstract: Retrieval based open-domain QA systems use retrieved documents and answer-span selection over retrieved documents to find best-answer candidates. We hypothesize that multilingual Question Answering (QA) systems are p... |
Title: Recipe for a General, Powerful, Scalable Graph Transformer Abstract: We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph ... |
Title: MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning Abstract: Many recent breakthroughs in multi-agent reinforcement learning (MARL) require the use of deep neural networks, which are challenging for human experts to interpret and understand. On the other hand, existing ... |
Title: Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space Abstract: We study the concentration phenomenon for discrete-time random dynamical systems with an unbounded state space. We develop a heuristic approach towards obtaining exponential concentration inequa... |
Title: FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech Abstract: We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, w... |
Title: Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation Abstract: Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Existing signal processing techniques for channel estimation do not s... |
Title: Generating Natural Language Proofs with Verifier-Guided Search Abstract: Deductive reasoning (drawing conclusions from assumptions) is a challenging problem in NLP. In this work, we focus on proof generation: given a hypothesis and a set of supporting facts in natural language, the model generates a proof tree i... |
Title: Lyapunov function approach for approximation algorithm design and analysis: with applications in submodular maximization Abstract: We propose a two-phase systematical framework for approximation algorithm design and analysis via Lyapunov function. The first phase consists of using Lyapunov function as an input a... |
Title: Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning Abstract: We study the privacy risks that are associated with training a neural network's weights with self-supervised learning algorithms. Through empirical evidence, we show that the fine-tuning stage, in which the network weights are ... |
Title: Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models Abstract: Knowledge Distillation (KD) is a prominent neural model compression technique which heavily relies on teacher network predictions to guide the training of a student model. Considering the ever-growing size of pre-tra... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.