LeArning Abstract

Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.

Papers

October 14, 2024

Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework
Zhengwei Yang, Yuke Li, Qiang Sun, Basura Fernando, Heng Huang, Zheng Wang
LeArning Abstract Shot Learning Multi Modal Unimodal Model
Neural networks that overcome classic challenges through practice
Kazuki Irie, Brenden M. Lake
Neural Network LeArning Abstract Strong Generalization Practice Mode Neural Network Model Open Challenge Cognitive Capability Metalearning Algorithm
Inverse Problems and Data Assimilation: A Machine Learning Approach
Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso, Andrew Stuart
LeArning Abstract Inverse Problem Visual Perspective Machine Learning Approach Data Assimilation MATH Dataset
Learning via Surrogate PAC-Bayes
Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj
LeArning Abstract Generalization Bound PAC Bayesian PAC Bayes Bound
KNN Transformer with Pyramid Prompts for Few-Shot Learning
Wenhao Li, Qiangchang Wang, Peng Zhao, Yilong Yin
LeArning Abstract Visual Prompt Transformer Based Network Effective Prompt Multi Modal PromPt Self Attention Matrix
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu
LeArning Abstract Molecular Property Molecular Data Structure Prediction Molecule Task Data Consistency

October 13, 2024

Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning
Buren Batu, Yuanmeng Liu, Tianyi Lyu
LeArning Abstract Multi Task Activity Recognition Prediction Accuracy Real Time Monitoring Formation Resistivity Limb Movement
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization
Alireza Salemi, Hamed Zamani
Large Language Model LeArning Abstract Retrieval Augmented Stable Rank Retrieval Augmentation Search Engine Knowledge Intensive Utility Maximization
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
Yanru Sun, Zongxia Xie, Emadeldeen Eldele, Dongyue Chen, Qinghua Hu, Min Wu
LeArning Abstract Time Series Time Series Forecasting Forecasting Performance Long Term Forecasting Real World Time Series
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, Takuma Seno
LeArning Abstract Many Parameter Deep Reinforcement Deep RL Normalization Layer Simplicity Bias
A Tidal Current Speed Forecasting Model based on Multiple Periodicity Learning
Tengfei Cheng, Yunxuan Dong, Yangdi Huang
LeArning Abstract Periodic Phenomenon Wavelet Neural Network Tidal Energy
Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data
Zhiqin Ma, Chunhua Zeng, Yi-Cheng Zhang, Thomas M. Bury
Machine Learning LeArning Abstract Complex System Surrogate Assisted Version Early Warning Historical Data Critical Exponent
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
Yiwei Zhang, Rouzbeh Behnia, Attila A. Yavuz, Reza Ebrahimi, Elisa Bertino
LeArning Abstract Secure Aggregation Federated Deep

October 12, 2024

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton
LeArning Abstract Procedural Fairness Fairness Aware Machine Fairness Assessment Fairness Measure Fairness Datasets
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Yunlu Yan, Lei Zhu, Yuexiang Li, Xinxing Xu, Rick Siow Mong Goh, Yong Liu, Salman Khan, Chun-Mei Feng
LeArning Abstract Federated Learning Domain Shift New Perspective Model Fairness Proportional Fairness Feature Shift
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
Subhankar Maity, Aniket Deroy
LeArning Abstract Natural Language Processing Generative AI Faithful Generation Question Generation Chain of Thought Prompting User Query
Bridging Text and Image for Artist Style Transfer via Contrastive Learning
Zhi-Song Liu, Li-Wen Wang, Jun Xiao, Vicky Kalogeiton
LeArning Abstract Text Modality Style Transfer Contrastive Example Image Stylization Image Style Transfer Arbitrary Style Transfer
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Yarden As, Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza, Stelian Coros, Andreas Krause
Reinforcement Learning LeArning Abstract Efficient Exploration Safety Constraint Positive Reinforcement Active Exploration Safe Exploration
Towards a Domain-Specific Modelling Environment for Reinforcement Learning
Natalie Sinani, Sahil Salma, Paul Boutot, Sadaf Mustafiz
Reinforcement Learning LeArning Abstract Positive Reinforcement Domain Specific Model Model Driven Modeling Language

October 11, 2024

Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity
Divyansh Jain, Eric Yang
LeArning Abstract Error Feedback SQL Generation Similarity Join

LeArning Abstract

Papers

Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework

Neural networks that overcome classic challenges through practice

Inverse Problems and Data Assimilation: A Machine Learning Approach

Learning via Surrogate PAC-Bayes

KNN Transformer with Pyramid Prompts for Few-Shot Learning

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning

Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

A Tidal Current Speed Forecasting Model based on Multiple Periodicity Learning

Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data

Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning

A New Perspective to Boost Performance Fairness for Medical Federated Learning

The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models

Bridging Text and Image for Artist Style Transfer via Contrastive Learning

ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning

Towards a Domain-Specific Modelling Environment for Reinforcement Learning

Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity