1.1k citations · 3.9k across the 29 of their papers we have counts for
13 papers · 1 filter
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Qingsong Lv, Ming Ding, Qiang Liu +7
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understandi…
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Qinkai Zheng, Xu Zou, Yuxiao Dong +5
Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers…
Modeling Protein Using Large-scale Pretrain Language Model
Yijia Xiao, Jiezhong Qiu, Ziang Li +2
Protein is linked to almost every life process. Therefore, analyzing the biological structure and property of protein sequences is critical to the exploration of life, as well as d…
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
Ingmar Kanitscheider, Joost Huizinga, David Farhi +9
An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks. If tasks depend on each other (e.g. needing to learn to walk before lear…
A Generalizable Approach to Learning Optimizers
Diogo Almeida, Clemens Winter, Jie Tang +1
A core issue with learning to optimize neural networks has been the lack of generalization to real world problems. To address this, we describe a system designed from a generalizat…
TDGIA:Effective Injection Attacks on Graph Neural Networks
Xu Zou, Qinkai Zheng, Yuxiao Dong +4
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. However, recent studies have shown that GNNs are vulnerable to adversarial atta…
FastMoE: A Fast Mixture-of-Expert Training System
Jiaao He, Jiezhong Qiu, Aohan Zeng +3
Mixture-of-Expert (MoE) presents a strong potential in enlarging the size of language model to trillions of parameters. However, training trillion-scale MoE requires algorithm and…
Self-supervised Learning: Generative or Contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou +4
Deep supervised learning has achieved great success in the last decade. However, its deficiencies of dependence on manual labels and vulnerability to attacks have driven people to…
Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI, :, Christopher Berner +24
On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as lo…
Dimensional Reweighting Graph Convolutional Networks
Xu Zou, Qiuye Jia, Jianwei Zhang +3
Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs. Though there exist various developments on sampling and aggregatio…
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh +9
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of…
Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
Zhengxiao Du, Chang Zhou, Ming Ding +2
Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently. However, few studies…
OpenAI Gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson +4
OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can s…