
OGBLSC: A LargeScale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over largescale ...
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ForceNet: A Graph Neural Network for LargeScale Quantum Calculations
With massive amounts of atomic simulation data available, there is a hug...
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WILDS: A Benchmark of intheWild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Catalyst discovery and optimization is key to solving many societal and ...
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An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
Scalable and costeffective solutions to renewable energy storage are es...
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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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Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Answering complex logical queries on largescale incomplete knowledge gr...
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Pretraining Graph Neural Networks
Many applications of machine learning in science and medicine, including...
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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Cosampling: Training Robust Networks for Extremely Noisy Supervision
Training robust deep networks is challenging under noisy labels. Current...
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Learning from Complementary Labels
Collecting labeled data is costly and thus a critical bottleneck in real...
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Learning Discrete Representations via Information Maximizing SelfAugmented Training
Learning discrete representations of data is a central machine learning ...
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Revisiting Distributionally Robust Supervised Learning in Classification
Distributionally Robust Supervised Learning (DRSL) is necessary for buil...
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Weihua Hu
verfied profile
Computer Science Ph.D. student at Stanford.