text stringlengths 0 4.09k |
|---|
Title: FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation Abstract: Given the high incidence and effective treatment options for liver diseases, they are of great socioeconomic importance. One of the most common methods for analyzing CT and MRI images for diagnosis and follow... |
Title: PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection Abstract: The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have an... |
Title: B\'ezier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization Abstract: In this paper, we propose a strategy to construct a multi-objective optimization algorithm from a single-objective optimization algorithm by using the B\'ezier simplex model. Also, we extend the stability of optimiza... |
Title: FL Games: A federated learning framework for distribution shifts Abstract: Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, whereby predictive... |
Title: Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization Abstract: At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially observable environments. Starting from widely used... |
Title: An improved neural network model for treatment effect estimation Abstract: Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes fo... |
Title: Learning to branch with Tree MDPs Abstract: State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learning branching rules from data has received increasing attention recently, and promising re... |
Title: Meta-Learning Regrasping Strategies for Physical-Agnostic Objects Abstract: Grasping inhomogeneous objects, practical use in real-world applications, remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. In this study, we propose a vision-based m... |
Title: DistilCamemBERT: a distillation of the French model CamemBERT Abstract: Modern Natural Language Processing (NLP) models based on Transformer structures represent the state of the art in terms of performance on very diverse tasks. However, these models are complex and represent several hundred million parameters ... |
Title: PyRelationAL: A Library for Active Learning Research and Development Abstract: In constrained real-world scenarios where it is challenging or costly to generate data, disciplined methods for acquiring informative new data points are of fundamental importance for the efficient training of machine learning (ML) mo... |
Title: KRNet: Towards Efficient Knowledge Replay Abstract: The knowledge replay technique has been widely used in many tasks such as continual learning and continuous domain adaptation. The key lies in how to effectively encode the knowledge extracted from previous data and replay them during current training procedure... |
Title: GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection Abstract: In recent years, the emergence and development of third-party platforms have greatly facilitated the growth of the Online to Offline (O2O) business. However, the large amount of transaction data raises new challe... |
Title: Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, the agent only receives preferences over trajec... |
Title: OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization Abstract: As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous networ... |
Title: Split personalities in Bayesian Neural Networks: the case for full marginalisation Abstract: The true posterior distribution of a Bayesian neural network is massively multimodal. Whilst most of these modes are functionally equivalent, we demonstrate that there remains a level of real multimodality that manifests... |
Title: Collaborative Adversarial Training Abstract: The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to local non-smoothness and steepness of normally obtained loss landscapes. Training augmented with adversa... |
Title: Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning Abstract: Learning to coordinate is a daunting problem in multi-agent reinforcement learning (MARL). Previous works have explored it from many facets, including cognition between agents, credit assignment, communication... |
Title: Time-series Transformer Generative Adversarial Networks Abstract: Many real-world tasks are plagued by limitations on data: in some instances very little data is available and in others, data is protected by privacy enforcing regulations (e.g. GDPR). We consider limitations posed specifically on time-series data... |
Title: Logarithmic regret bounds for continuous-time average-reward Markov decision processes Abstract: We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting. In contrast to discrete-time MDPs, a continuous-time process moves to a state a... |
Title: How Powerful are Spectral Graph Neural Networks Abstract: Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on graph signal filters, and some models able to learn arbitrary spectral filters have emerged recently. However, few works analyze the expressive power of spectral GNNs. This pap... |
Title: An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments Abstract: In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable. Among the numerous a... |
Title: Deep Image Retrieval is not Robust to Label Noise Abstract: Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to label noise even in popular datasets. As previous works primarily studied ann... |
Title: Exploration of the possibility of infusing Social Media Trends into generating NFT Recommendations Abstract: Recommendations Systems have been identified to be one of the integral elements of driving sales in e-commerce sites. The utilization of opinion mining data extracted from trends has been attempted to imp... |
Title: Deep Neural Network approaches for Analysing Videos of Music Performances Abstract: This paper presents a framework to automate the labelling process for gestures in musical performance videos with a 3D Convolutional Neural Network (CNN). While this idea was proposed in a previous study, this paper introduces se... |
Title: Poincar\'{e} Heterogeneous Graph Neural Networks for Sequential Recommendation Abstract: Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of SR generally present the intrinsic power-la... |
Title: 2-d signature of images and texture classification Abstract: We introduce a proper notion of 2-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a 2-dimensional object such as an image. It thus serves as a low-dimensional fea... |
Title: Hyperspectral Image Classification With Contrastive Graph Convolutional Network Abstract: Recently, Graph Convolutional Network (GCN) has been widely used in Hyperspectral Image (HSI) classification due to its satisfactory performance. However, the number of labeled pixels is very limited in HSI, and thus the av... |
Title: Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases Abstract: Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation complexity. In this work, we propose gene expression dim... |
Title: A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization Abstract: Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantization can result in signif... |
Title: [Re] Distilling Knowledge via Knowledge Review Abstract: This effort aims to reproduce the results of experiments and analyze the robustness of the review framework for knowledge distillation introduced in the CVPR '21 paper 'Distilling Knowledge via Knowledge Review' by Chen et al. Previous works in knowledge d... |
Title: Efficient Mixed Dimension Embeddings for Matrix Factorization Abstract: Despite the prominence of neural network approaches in the field of recommender systems, simple methods such as matrix factorization with quadratic loss are still used in industry for several reasons. These models can be trained with alterna... |
Title: Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data Abstract: Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of mul... |
Title: Manifold-aligned Neighbor Embedding Abstract: In this paper, we introduce a neighbor embedding framework for manifold alignment. We demonstrate the efficacy of the framework using a manifold-aligned version of the uniform manifold approximation and projection algorithm. We show that our algorithm can learn an al... |
Title: Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting Abstract: Due to the practical importance of regular expressions (regexes, for short), there has been a lot of research to automatically generate regexes from positive and negative string examples. We tackle the problem of learning regexes fas... |
Title: Adaptive Fairness-Aware Online Meta-Learning for Changing Environments Abstract: The fairness-aware online learning framework has arisen as a powerful tool for the continual lifelong learning setting. The goal for the learner is to sequentially learn new tasks where they come one after another over time and the ... |
Title: What You See is What You Classify: Black Box Attributions Abstract: An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nat... |
Title: Fed-DART and FACT: A solution for Federated Learning in a production environment Abstract: Federated Learning as a decentralized artificial intelligence (AI) solution solves a variety of problems in industrial applications. It enables a continuously self-improving AI, which can be deployed everywhere at the edge... |
Title: RL with KL penalties is better viewed as Bayesian inference Abstract: Reinforcement learning (RL) is frequently employed in fine-tuning large language models (LMs), such as GPT-3, to penalize them for undesirable features of generated sequences, such as offensiveness, social bias, harmfulness or falsehood. The R... |
Title: Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity Abstract: Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plastici... |
Title: Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction Abstract: How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large amount of annotation for training deep learning models. Annotating molecules, however, i... |
Title: SelfReformer: Self-Refined Network with Transformer for Salient Object Detection Abstract: The global and local contexts significantly contribute to the integrity of predictions in Salient Object Detection (SOD). Unfortunately, existing methods still struggle to generate complete predictions with fine details. T... |
Title: Active Learning Through a Covering Lens Abstract: Deep active learning aims to reduce the annotation cost for deep neural networks, which are notoriously data-hungry. Until recently, deep active learning methods struggled in the low-budget regime, where only a small amount of samples are annotated. The situation... |
Title: Learning heterophilious edge to drop: A general framework for boosting graph neural networks Abstract: Graph Neural Networks (GNNs) aim at integrating node contents with graph structure to learn nodes/graph representations. Nevertheless, it is found that most of existing GNNs do not work well on data with high h... |
Title: Towards automatic detection of wildlife trade using machine vision models Abstract: Unsustainable trade in wildlife is one of the major threats affecting the global biodiversity crisis. An important part of the trade now occurs on the internet, especially on digital marketplaces and social media. Automated metho... |
Title: ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification Abstract: Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, unlabeled nodes for the given graph usually fo... |
Title: ScholarBERT: Bigger is Not Always Better Abstract: Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have shown impressive performance on various downstream tasks. Increasingly, researchers are "finetuning" these models to improve performance on domain-specific tasks.... |
Title: POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning Abstract: The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved. Although agents initial... |
Title: Capacity Bounds for the DeepONet Method of Solving Differential Equations Abstract: In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems. A particularly active area in this theme has been "physics informed machine learning" [1] which foc... |
Title: Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent Abstract: Recent studies have shown that gradient descent (GD) can achieve improved generalization when its dynamics exhibits a chaotic behavior. However, to obtain the desired effect, the step-size should be chosen sufficiently la... |
Title: Graph-Based Methods for Discrete Choice Abstract: Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete cho... |
Title: Statistical inference as Green's functions Abstract: Statistical inference from data is foundational task in science. Recently, it receives growing attention for its central role in inference systems of primary interest in data science, artificial intelligence, or machine learning. However, the understanding of ... |
Title: User Clustering for Rate Splitting using Machine Learning Abstract: Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among ... |
Title: Exploring the limits of multifunctionality across different reservoir computers Abstract: Multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we explore the performance of a continuous-time, leaky-integrator, and next-generation `re... |
Title: Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems Abstract: Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor improvement of 1.8 dB and 1.2 dB, respectively, ov... |
Title: Markedness in Visual Semantic AI Abstract: We evaluate the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for biases related to the marking of age, gender, and race or ethnicity. Given the option to label an image as "a photo of a person" or to select a label ... |
Title: StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models Abstract: Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia. However, our world is dynamic, evolves over... |
Title: Generic bounds on the approximation error for physics-informed (and) operator learning Abstract: We propose a very general framework for deriving rigorous bounds on the approximation error for physics-informed neural networks (PINNs) and operator learning architectures such as DeepONets and FNOs as well as for p... |
Title: Fine-Grained Counting with Crowd-Sourced Supervision Abstract: Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis further. In this work we study the potential to do so on the challengin... |
Title: Causal Machine Learning for Healthcare and Precision Medicine Abstract: Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react ... |
Title: Variable-Input Deep Operator Networks Abstract: Existing architectures for operator learning require that the number and locations of sensors (where the input functions are evaluated) remain the same across all training and test samples, significantly restricting the range of their applicability. We address this... |
Title: Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy Abstract: We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algor... |
Title: Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees Abstract: We propose Instance-Based Uncertainty estimation for Gradient-boosted regression trees~(IBUG), a simple method for extending any GBRT point predictor to produce probabilistic predictions. IBUG computes a non-parametric distribu... |
Title: Spreading Factor and RSSI for Localization in LoRa Networks: A Deep Reinforcement Learning Approach Abstract: Recent advancements in Internet of Things (IoT) technologies have resulted in a tightening of requirements from various applications including localization in LoRa networks. To address the growing demand... |
Title: Logical Reasoning with Span Predictions: Span-level Logical Atoms for Interpretable and Robust NLI Models Abstract: Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models are known to learn ... |
Title: Informed Pre-Training on Prior Knowledge Abstract: When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that have been pre-trained on other large data sets, pre-training on more concise forms o... |
Title: SiPRNet: End-to-End Learning for Single-Shot Phase Retrieval Abstract: Traditional optimization algorithms have been developed to deal with the phase retrieval problem. However, multiple measurements with different random or non-random masks are needed for giving a satisfactory performance. This brings a burden ... |
Title: Federated Distillation based Indoor Localization for IoT Networks Abstract: Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithm... |
Title: Overfitting in quantum machine learning and entangling dropout Abstract: The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to... |
Title: Data augmentation for efficient learning from parametric experts Abstract: We present a simple, yet powerful data-augmentation technique to enable data-efficient learning from parametric experts for reinforcement and imitation learning. We focus on what we call the policy cloning setting, in which we use online ... |
Title: What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability Abstract: Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation tha... |
Title: CELEST: Federated Learning for Globally Coordinated Threat Detection Abstract: The cyber-threat landscape has evolved tremendously in recent years, with new threat variants emerging daily, and large-scale coordinated campaigns becoming more prevalent. In this study, we propose CELEST (CollaborativE LEarning for ... |
Title: Advanced Transient Diagnostic with Ensemble Digital Twin Modeling Abstract: The use of machine learning (ML) model as digital-twins for reduced-order-modeling (ROM) in lieu of system codes has grown traction over the past few years. However, due to the complex and non-linear nature of nuclear reactor transients ... |
Title: Exploiting the Curvature of Feasible Sets for Faster Projection-Free Online Learning Abstract: In this paper, we develop new efficient projection-free algorithms for Online Convex Optimization (OCO). Online Gradient Descent (OGD) is an example of a classical OCO algorithm that guarantees the optimal $O(\sqrt{T})... |
Title: Rethinking Streaming Machine Learning Evaluation Abstract: While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are perf... |
Title: Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images Abstract: Traditionally anomaly detection (AD) is treated as an unsupervised problem utilizing only normal samples due to the intractability of characterizing everything that looks unlike the normal data. However, it has recent... |
Title: Learning differential equations from data Abstract: Differential equations are used to model problems that originate in disciplines such as physics, biology, chemistry, and engineering. In recent times, due to the abundance of data, there is an active search for data-driven methods to learn Differential equation... |
Title: Conditional Supervised Contrastive Learning for Fair Text Classification Abstract: Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data. However, the learned representations could potentially lead to performa... |
Title: Robust and Agnostic Learning of Conditional Distributional Treatment Effects Abstract: The conditional average treatment effect (CATE) is the best point prediction of individual causal effects given individual baseline covariates and can help personalize treatments. However, as CATE only reflects the (conditiona... |
Title: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Abstract: We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding tex... |
Title: Flexible Diffusion Modeling of Long Videos Abstract: We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitrary subset ... |
Title: Domain Adaptation for Memory-Efficient Dense Retrieval Abstract: Dense retrievers encode documents into fixed dimensional embeddings. However, storing all the document embeddings within an index produces bulky indexes which are expensive to serve. Recently, BPR (Yamada et al., 2021) and JPQ (Zhan et al., 2021a) ... |
Title: What Makes Data-to-Text Generation Hard for Pretrained Language Models? Abstract: Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models(PL... |
Title: Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering Abstract: Federated learning is generally used in tasks where labels are readily available (e.g., next word prediction). Relaxing this constraint requires design of unsupervised learning techniques that can support desirable properties... |
Title: Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs Abstract: Recent studies have shown that episodic reinforcement learning (RL) is not more difficult than contextual bandits, even with a long planning horizon and unknown state transitions. However, these results are limited to... |
Title: Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods Abstract: Self-Supervised Learning (SSL) surmises that inputs and pairwise positive relationships are enough to learn meaningful representations. Although SSL has recently reached a milestone: outperformi... |
Title: Information Propagation by Composited Labels in Natural Language Processing Abstract: In natural language processing (NLP), labeling on regions of text, such as words, sentences and paragraphs, is a basic task. In this paper, label is defined as map between mention of entity in a region on text and context of en... |
Title: Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net Abstract: Cardiomegaly is indeed a medical disease in which the heart is enlarged. Cardiomegaly is better to handle if caught early, so early detection is critical. The chest X-ray, being one of the most often used radiography examinations... |
Title: Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation Abstract: Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as ... |
Title: FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing Abstract: Fast identification of new network attack patterns is crucial for improving network security. Nevertheless, identifying an ongoing attack in a heterogeneous network is a non-trivial task. Federated ... |
Title: Identifying (anti-)skyrmions while they form Abstract: We use a Convolutional Neural Network (CNN) to identify the relevant features in the thermodynamical phases of a simulated three-dimensional spin-lattice system with ferromagnetic and Dzyaloshinskii-Moriya (DM) interactions. Such features include (anti-)skyr... |
Title: Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning Abstract: We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework for efficient training with limited model-specif... |
Title: BolT: Fused Window Transformers for fMRI Time Series Analysis Abstract: Functional magnetic resonance imaging (fMRI) enables examination of inter-regional interactions in the brain via functional connectivity (FC) analyses that measure the synchrony between the temporal activations of separate regions. Given the... |
Title: PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning Abstract: Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in F... |
Title: uGLAD: Sparse graph recovery by optimizing deep unrolled networks Abstract: Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models... |
Title: Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment Abstract: Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown that the accuracy and robustness of unsupervised word translation (UWT) can be... |
Title: Identifying Patient-Specific Root Causes of Disease Abstract: Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many significant variables with inconsequential effect sizes. A few highly ... |
Title: Forecasting of Non-Stationary Sales Time Series Using Deep Learning Abstract: The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes ... |
Title: DOGE-Train: Discrete Optimization on GPU with End-to-end Training Abstract: We present a fast, scalable, data-driven approach for solving linear relaxations of 0-1 integer linear programs using a graph neural network. Our solver is based on the Lagrange decomposition based algorithm FastDOG (Abbas et al. (2022))... |
Title: Generalization Gap in Amortized Inference Abstract: The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression. In this work, we study the generalizations of a popular class of probabilistic models - the Variatio... |
Title: Machine Learning for Electricity Market Clearing Abstract: This paper seeks to design a machine learning twin of the optimal power flow (OPF) optimization, which is used in market-clearing procedures by wholesale electricity markets. The motivation for the proposed approach stems from the need to obtain the digi... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.