793 citations · 3.2k across the 19 of their papers we have counts for
19 papers
Using a Bayesian-Inference Approach to Calibrating Models for Simulation in Robotics
Huzaifa Mustafa Unjhawala, Ruochun Zhang, Wei Hu +3
In robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simula…
GraphCast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson +15
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute reso…
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Weihua Hu, Kaidi Cao, Kexin Huang +4
Despite recent advances in Graph Neural Networks (GNNs), their training strategies remain largely under-explored. The conventional training strategy learns over all nodes in the or…
Progressive Optimal Path Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks
Xuanxi Zhang, Jihao Long, Wei Hu +2
Closed-loop optimal control design for high-dimensional nonlinear systems has been a long-standing challenge. Traditional methods, such as solving the associated Hamilton-Jacobi-Be…
Learning Backward Compatible Embeddings
Weihua Hu, Rajas Bansal, Kaidi Cao +3
Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding tea…
Solving Optimal Control Problems of Rigid-Body Dynamics with Collisions Using the Hybrid Minimum Principle
Wei Hu, Jihao Long, Yaohua Zang +2
Collisions are common in many dynamical systems with real applications. They can be formulated as hybrid dynamical systems with discontinuities automatically triggered when states…
A Machine Learning Enhanced Algorithm for the Optimal Landing Problem
Yaohua Zang, Jihao Long, Xuanxi Zhang +3
We propose a machine learning enhanced algorithm for solving the optimal landing problem. Using Pontryagin's minimum principle, we derive a two-point boundary value problem for the…
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das +5
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based ca…
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren +3
Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific a…
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das +14
Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reli…
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal +14
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer produc…
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik +5
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML…
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Hongyu Ren, Weihua Hu, Jure Leskovec
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to…
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes +4
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific lab…
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec +1
Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a n…
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu +5
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during train…
Learning from Complementary Labels
Takashi Ishida, Gang Niu, Weihua Hu +1
Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from comp…
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu, Takeru Miyato, Seiya Tokui +2
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes cluster…
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu, Gang Niu, Issei Sato +1
Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance…