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

July 29, 2024

Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation
Manish Bhattarai, Javier E. Santos, Shawn Jones, Ayan Biswas, Boian Alexandrov, Daniel O'Malley
Large Language Model Language Model LeArning Abstract Retrieval Augmented Generation Code Translation

July 28, 2024

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai
LeArning Abstract Medical Information Mart for Intensive Forecasting Task Patient Representation Patient Trajectory Clinical Outcome Prediction

July 25, 2024

Learning Physics-Consistent Material Behavior Without Prior Knowledge
Zhichao Han, Mohit Pundir, Olga Fink, David S. Kammer
LeArning Abstract Prior Knowledge Strain Energy Stress Strain

July 22, 2024

July 21, 2024

Learning to Compile Programs to Neural Networks
Logan Weber, Jesse Michel, Alex Renda, Michael Carbin
Neural Network LeArning Abstract Neural Surrogate

July 20, 2024

Seal: Advancing Speech Language Models to be Few-Shot Learners
Shuyu Lei, Lingen Liu, Jiaolong Yang, Yasen Jiao, Yuxiang Yang, Yushu Yang, Xiang Guo
LeArning Abstract Speech Language Model Frozen Language Model Seal Generation

July 19, 2024

July 18, 2024

LeArning Abstract

Papers

Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction

Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging

Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation

Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations

Learning Physics-Consistent Material Behavior Without Prior Knowledge

Learning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogates

Learning to Play Foosball: System and Baselines

Stochastic weight matrix dynamics during learning and Dyson Brownian motion

Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information

Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance Modelling

Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation

Learning to Compile Programs to Neural Networks

Seal: Advancing Speech Language Models to be Few-Shot Learners

Value Internalization: Learning and Generalizing from Social Reward

Byzantine-tolerant distributed learning of finite mixture models

Privacy-preserving gradient-based fair federated learning

Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer

Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation