597 citations · 2.3k across the 62 of their papers we have counts for
63 papers
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity
Pengyun Yue, Hanzhen Zhao, Cong Fang +4
With distributed machine learning being a prominent technique for large-scale machine learning tasks, communication complexity has become a major bottleneck for speeding up trainin…
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Ruichen Li, Haotian Ye, Du Jiang +8
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of exis…
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective
Guhao Feng, Bohang Zhang, Yuntian Gu +3
Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically improve the performance of Large Language Models (LLMs), particularly when dealing with comple…
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
Bohang Zhang, Guhao Feng, Yiheng Du +2
Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs). While numerous architectures have been proposed, so far there…
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
Krzysztof Marcin Choromanski, Shanda Li, Valerii Likhosherstov +7
We propose a new class of linear Transformers called FourierLearner-Transformers (FLTs), which incorporate a wide range of relative positional encoding mechanisms (RPEs). These inc…
Rethinking the Expressive Power of GNNs via Graph Biconnectivity
Bohang Zhang, Shengjie Luo, Liwei Wang +1
Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs in terms of the…