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07/03/2023
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challengi...
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12/24/2022
GraphCast: Learning skillful medium-range global weather forecasting
We introduce a machine-learning (ML)-based weather simulator–called "Gra...
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10/26/2022
TuneUp: A Training Strategy for Improving Generalization of Graph Neural Networks
Despite many advances in Graph Neural Networks (GNNs), their training st...
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06/07/2022
Learning Backward Compatible Embeddings
Embeddings, low-dimensional vector representation of objects, are fundam...
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12/09/2021
Extending the WILDS Benchmark for Unsupervised Adaptation
Machine learning systems deployed in the wild are often trained on a sou...
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03/17/2021
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale ...
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03/02/2021
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
With massive amounts of atomic simulation data available, there is a hug...
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12/14/2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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10/20/2020
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Catalyst discovery and optimization is key to solving many societal and ...
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10/14/2020
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
Scalable and cost-effective solutions to renewable energy storage are es...
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05/02/2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging ...
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02/14/2020
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Answering complex logical queries on large-scale incomplete knowledge gr...
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05/29/2019
Pre-training Graph Neural Networks
Many applications of machine learning in science and medicine, including...
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10/01/2018
How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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04/18/2018
Co-sampling: Training Robust Networks for Extremely Noisy Supervision
Training robust deep networks is challenging under noisy labels. Current...
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05/22/2017
Learning from Complementary Labels
Collecting labeled data is costly and thus a critical bottleneck in real...
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02/28/2017
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning ...
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11/07/2016