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Found 6 papers, 6 papers with code
Information Flow in Self-Supervised Learning
In this paper, we conduct a comprehensive analysis of two dual-branch (Siamese architecture) self-supervised learning approaches, namely Barlow Twins and spectral contrastive learning, through the lens of matrix mutual information.
Cumulative Reasoning with Large Language Models
We demonstrate CR's superiority through several complex reasoning tasks: it outperforms existing methods in logical inference tasks with up to a 9. 3% improvement, achieving 98. 04% accuracy on the curated FOLIO wiki dataset.
Ranked #14 on
Math Word Problem Solving
on MATH
Matrix Information Theory for Self-Supervised Learning
Inspired by this framework, we introduce Matrix-SSL, a novel approach that leverages matrix information theory to interpret the maximum entropy encoding loss as matrix uniformity loss.
Ranked #1 on
Contrastive Learning
on imagenet-1k
RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data.
Contrastive Learning Is Spectral Clustering On Similarity Graph
Contrastive learning is a powerful self-supervised learning method, but we have a limited theoretical understanding of how it works and why it works.
Few-Shot Generative Conversational Query Rewriting
Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing information retrieval systems.
