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Found 20 papers, 14 papers with code
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Extensive evaluation of TuneUp on five diverse GNN architectures, three types of prediction tasks, and both transductive and inductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes.
Learning Backward Compatible Embeddings
We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
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 applications.
Ranked #1 on
Knowledge Graphs
on WikiKG90M-LSC
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency.
ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation
We use ForceNet to perform quantum chemistry simulations, where ForceNet is able to achieve 4x higher success rate than existing ML models.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
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) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
Ranked #3 on
Complex Query Answering
on FB15k-237
Strategies for Pre-training Graph Neural Networks
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 labels are scarce during training.
Ranked #2 on
Drug Discovery
on ClinTox
How Powerful are Graph Neural Networks?
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Ranked #1 on
Graph Classification
on COX2
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
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 training.
Ranked #7 on
Learning with noisy labels
on CIFAR-10N-Random3
Learning from Complementary Labels
Collecting complementary labels would be less laborious than collecting ordinary labels, since users do not have to carefully choose the correct class from a long list of candidate classes.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Ranked #3 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Since the DRSL is explicitly formulated for a distribution shift scenario, we naturally expect it to give a robust classifier that can aggressively handle shifted distributions.
