Strong Generalization

Strong generalization, the ability of machine learning models to perform well on unseen data, is a central objective in current research. Active areas of investigation include improving the robustness of self-supervised learning, understanding the optimization dynamics of transformers and other architectures (including CNNs and RNNs), and developing methods to enhance generalization through data augmentation, regularization techniques (e.g., logical regularization, consistency regularization), and improved training strategies (e.g., few-shot learning, meta-learning). These advancements are crucial for building reliable and adaptable AI systems across diverse applications, from image classification and natural language processing to healthcare and robotics.

1046papers

Papers - Page 33

December 28, 2023

Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space
Padmaksha Roy, Tyler Cody, Himanshu Singhal, Kevin Choi, Ming Jin
Domain Invariant Representation Intrusion Detection Latent Space Strong Generalization Multi Task Learning Domain Generalization Method

December 27, 2023

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
Sebastian Dziadzio, Çağatay Yıldız, Gido M. van de Ven, Tomasz Trzciński, Tinne Tuytelaars, Matthias Bethge
Catastrophic Forgetting Disentangled Learning Continual Learning Strong Generalization Continual Text Classification Memory Editing Continual LEArning

December 26, 2023

Generalization in Kernel Regression Under Realistic Assumptions
Daniel Barzilai, Ohad Shamir
Parameterized Model Modeling Assumption Strong Generalization Kernel Regression Kernel Regime Benign Overfitting

December 25, 2023

Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
Songming Zhang, Yuxiao Luo, Qizhou Wang, Haoang Chi, Xiaofeng Chen, Bo Han, Jinyan Li
Distribution Generalization Training Data Data Mixture Strong Generalization Generalization Error Generalization Problem

December 23, 2023

December 21, 2023

Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction
Wenhui Cui, Haleh Akrami, Ganning Zhao, Anand A. Joshi, Richard M. Leahy
Action Feature Self Supervised Learning Meta Transfer Strong Generalization Functional Magnetic Resonance Imaging Seizure Prediction Foundation Model Traumatic Brain

December 20, 2023

SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning
William Heyden, Habib Ullah, M. Salman Siddiqui, Fadi Al Machot
Generalized Zero Shot Learning Strong Generalization Semantic Class Unseen Class Semantic Description Generalization Gap Representation Learning

December 19, 2023

Optimizing Neural Networks with Gradient Lexicase Selection
Li Ding, Lee Spector
Generalization Performance Strong Generalization Neural Network Lexicase Selection

December 14, 2023

Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning
Martin Riedmiller, Tim Hertweck, Roland Hafner
Strong Generalization Multi Task Reinforcement Learning Near Optimal Executor Design Principle Generalization Property

December 12, 2023

December 10, 2023

The Generalization Gap in Offline Reinforcement Learning
Ishita Mediratta, Qingfei You, Minqi Jiang, Roberta Raileanu
Strong Generalization Offline Learning Generalization Gap Offline Reinforcement Learning Online Reinforcement Learning

December 8, 2023

LLM Interactive Optimization of Open Source Python Libraries -- Case Studies and Generalization
Andreas Florath
LLM Based Open Source Python Source Code Code Optimization Large Language Model Strong Generalization Case Study

December 7, 2023

Rapid Motor Adaptation for Robotic Manipulator Arms
Yichao Liang, Kevin Ellis, João Henriques
Manipulation Skill Strong Generalization Motor Adaptation Generalization Performance

December 6, 2023

Strong Generalization

Papers - Page 33

Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization

Generalization in Kernel Regression Under Realistic Assumptions

Mixture Data for Training Cannot Ensure Out-of-distribution Generalization

MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning

Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations

A Theory of Non-Acyclic Generative Flow Networks

Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction

SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning

Optimizing Neural Networks with Gradient Lexicase Selection

Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning

BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

GenDet: Towards Good Generalizations for AI-Generated Image Detection

The Generalization Gap in Offline Reinforcement Learning

LLM Interactive Optimization of Open Source Python Libraries -- Case Studies and Generalization

Rapid Motor Adaptation for Robotic Manipulator Arms

Interpretability Illusions in the Generalization of Simplified Models

Generalization to New Sequential Decision Making Tasks with In-Context Learning

Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation