4.5k citations · 11.2k across the 85 of their papers we have counts for
9 papers · 1 filter
Efficient Automatic Machine Learning via Design Graphs
Shirley Wu, Jiaxuan You, Jure Leskovec +1
Despite the success of automated machine learning (AutoML), which aims to find the best design, including the architecture of deep networks and hyper-parameters, conventional AutoM…
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
Aosong Feng, Irene Li, Yuang Jiang +1
Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity. Sparse Transformer is a popular approach to improving t…
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
Syed Asad Rizvi, Nazreen Pallikkavaliyaveetil, David Zhang +15
Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstru…
How Powerful is Implicit Denoising in Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +3
Graph Neural Networks (GNNs), which aggregate features from neighbors, are widely used for graph-structured data processing due to their powerful representation learning capabiliti…
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Songtao Liu, Zhengkai Tu, Minkai Xu +6
Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-ste…
FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao +32
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the…
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
Tailin Wu, Qinchen Wang, Yinan Zhang +7
Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications…
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Kenza Amara, Rex Ying, Zitao Zhang +5
As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increas…
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…