AI Technology & Industry Review
In the new paper Exphormer: Sparse Transformers for Graphs, a team from the University of British Columbia, Google Research and the Alberta Machine Intelligence Institute proposes Exphormer, a class of graph transformers with improved scalability and reduced computational complexity that achieves state-of-the-art performance on graph benchmarks.
In the new paper GraphCast: Learning Skillful Medium-Range Global Weather Forecasting, a research team from DeepMind and Google presents GraphCast, a machine-learning (ML)-based weather simulator that scales well with data and can generate a 10-day forecast in under 60 seconds. GraphCast outperforms the world’s most accurate deterministic operational medium-range weather forecasting system and betters existing ML-based benchmarks.
In the new paper TF-GNN: Graph Neural Networks in TensorFlow, a research team from Google Core ML, Google Research, and DeepMind open-sources the TensorFlow GNN (TF-GNN) scalable library, which leverages heterogeneous relational data to create graph neural network models.
A research team from Yale and IBM presents Kernel Graph Neural Networks (KerGNNs), which integrate graph kernels into the message passing process of GNNs in one framework, achieving performance comparable to state-of-the-art methods and significantly improving model interpretability compared with conventional GNNs.
A research team from Google Brain, Columbia University and University of Oxford proposes Graph Kernel Attention Transformers (GKATs), a new class of graph neural network that achieves greater expressive power than SOTA GNNs while reducing computation burdens.
MIT and University of Waterloo researchers propose a machine learning model that outperforms the average student on problems from MIT’s 6.036 Introduction to Machine Learning course.
A research team from University of Cambridge, Imperial College London & Twitter, UCLA, MPI-MIS, and SJTU & UNSW proposes CW Networks (CWNs), a message-passing scheme that operates on regular cell complexes and achieves stronger expressive power than graph neural networks (GNNs).
Cornell and Facebook AI “Correct and Smooth” high-accuracy graph learning method is fast to train and outperforms big Graph Neural Network (GNN) models
DeepMind unveiled a partnership with Google Maps that has leveraged advanced GNNs to improve ETA accuracy.
Researchers propose a flexible GNN benchmarking framework that can also accommodate the needs of researchers to add new datasets and models.
The MXNet team and the Amazon Web Services AI lab recently teamed up with New York University / NYU Shanghai to announce Deep Graph Library (DGL), a Python package that provides easy implementations of GNNs research. Some fast takeaways on DGL: