Statistical Learning
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
We propose Any2Graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input.
Multi-Scale Representation Learning for Protein Fitness Prediction
Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets.
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis
The remarkable success of transformers in sequence modeling tasks, spanning various applications in natural language processing and computer vision, is attributed to the critical role of self-attention.
From Causal to Concept-Based Representation Learning
A key question in this growing field is to provide rigorous conditions under which latent factors can be identified and thus, potentially learned.
Bayesian Learning via Q-Exponential Process
Regularization is one of the most fundamental topics in optimization, statistics and machine learning.
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening Guy Bar-Shalom
This paper introduces a new Subgraph GNN framework to address these issues.
Training Chain-of-Thought via Latent-Variable Inference Du Phan Matthew D. Hoffman
One can also improve LLMs' performance on a specific task by