Learning Model

Learning models encompass a broad range of computational methods designed to extract patterns and make predictions from data, with primary objectives focused on accuracy, efficiency, and robustness. Current research emphasizes improving model scalability and transferability across diverse data types and domains, exploring architectures like graph neural networks, transformers, and mixtures-of-experts, as well as techniques such as federated learning and curriculum learning. These advancements have significant implications for various fields, including robotics, natural language processing, and healthcare, by enabling more efficient data analysis, improved decision-making, and the development of more adaptable and reliable systems.

Papers

May 16, 2022

Deep Apprenticeship Learning for Playing Games
Dejan Markovikj
Reinforcement Learning Deep Learning Deep Neural Network Artificial Agent Learning Model Game Play Apprenticeship Learning

May 9, 2022

Research on the correlation between text emotion mining and stock market based on deep learning
Chenrui Zhang
Deep Learning BERT Model DH Research Total Correlation Learning Model Stock Market Emotion Analysis Investor Sentiment Financial Corpus

April 16, 2022

Theory of Graph Neural Networks: Representation and Learning
Stefanie Jegelka
Neural Network LeArning Abstract Graph Neural Network Theoretical Understanding Individual Representation Learning Model Order Graph Neural Network

March 14, 2022

Simulating Liquids with Graph Networks
Jonathan Klimesch, Philipp Holl, Nils Thuerey
Deep Learning Model Strong Generalization Fluid Dynamic Graph Network Learning Model

March 11, 2022

A New Learning Paradigm for Stochastic Configuration Network: SCN+
Yanshuang Ao, Xinyu Zhou, Wei Dai
Incremental Learning Learning Model Privileged Information New Learning Stochastic Configuration Network

March 2, 2022

Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection
Venkata Beri
Computer Vision Deep Convolutional Neural Network Object Detection Model Object Recognition Efficient Hybrid Learning Model Deep Learning Based Object Detection Object Identification

January 26, 2022

How Robust are Discriminatively Trained Zero-Shot Learning Models?
Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Pinar Duygulu
Adversarial Attack Zero Shot Learning Learning Model Image Corruption Zero Shot Adversarial Robustness

January 10, 2022

Continual Learning of Long Topic Sequences in Neural Information Retrieval
Thomas Gerald, Laure Soulier
Continual LEArning Information Retrieval Long Sequence Learning Model Neural Information Retrieval

December 4, 2021

Towards the One Learning Algorithm Hypothesis: A System-theoretic Approach
Christos Mavridis, John Baras
Human Cognition Learning Algorithm Hierarchical Attention Learning Model Progressive Learning Learning Architecture System Theoretic

December 1, 2021

SaDe: Learning Models that Provably Satisfy Domain Constraints
Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
Gradient Descent Regularization Model Learning Model Constraint Satisfaction Domain Constraint

November 15, 2018

A Spectral View of Adversarially Robust Features
Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
Native Robustness Adversarial Perturbation Spectral Feature Learning Model