2.3k citations · 2.5k across the 19 of their papers we have counts for
5 papers · 1 filter
SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences
Peng Qi, Guangtao Wang, Jing Huang
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks
Jing Huang, Jie Yang
Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domain…
Multi-hop Attention Graph Neural Network
Guangtao Wang, Rex Ying, Jing Huang +1
Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is co…
Multiple instance learning with graph neural networks
Ming Tu, Jing Huang, Xiaodong He +1
Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN)…