Graph Neural Networks (GNN)

11 Posts

Long-Range Weather Forecasts: This ML-based forecast simulator outperformed medium-range forecast systems.
Graph Neural Networks (GNN)

Long-Range Weather Forecasts: This ML-based forecast simulator outperformed medium-range forecast systems.

Machine learning models have predicted weather a few days ahead of time. A new approach substantially extends the time horizon. Remi Lam and colleagues at Google developed GraphCast, a weather-forecasting system based on graph neural networks (GNNs).
Overall architecture of GEM.
Graph Neural Networks (GNN)

What a Molecule’s Structure Reveals: Baidu Creates AI to Classify Molecular Properties

The authors trained a modified GNN on a dataset of 18 million molecules to find molecular properties.
Graph Transformer with positional encoding
Graph Neural Networks (GNN)

A Transformer for Graphs: New Method for Processing Graph Data with Transformers

Transformers can learn a lot from sequential data like words in a book, but they’ve shown limited ability to learn from data in the form of a graph. A new transformer variant gives graphs due attention.
Animation showing optimizing a physical design
Graph Neural Networks (GNN)

Airfoils Automatically Optimized: DeepMind AI Research Simulates Fluid Dynamics

Engineers who design aircraft, aqueducts, and other objects that interact with air and water use numerical simulations to test potential shapes, but they rely on trial and error to improve their designs. A neural simulator can optimize the shape itself.
Overview of Graph Hyper Network (GHN-2)
Graph Neural Networks (GNN)

Who Needs Training? Graph neural network selects optimal weights for image tasks.

When you’re training a neural network, it takes a lot of computation to optimize its weights using an iterative algorithm like stochastic gradient descent. Wouldn’t it be great to compute the best parameter values in one pass? A new method takes a substantial step in that direction.
Diagram with automated decision systems
Graph Neural Networks (GNN)

Roadblocks to Regulation: Why laws to regulate AI usually fail.

Most U.S. state agencies use AI without limits or oversight. An investigative report probed reasons why efforts to rein them in have made little headway. Since 2018, nearly every proposed bill aimed at studying or controlling how state agencies use automated decision systems.
Animated illustration shows the model architecture of a graph neural network.
Graph Neural Networks (GNN)

A Deeper Look at Graphs: Graph Neural Networks Work Better With More Layers

New research shows that drastically increasing the number of layers in a graph neural networks improves its performance on large datasets.
Data related to DeepCE, a system designed to predict how particular drugs will influence the amounts of RNA
Graph Neural Networks (GNN)

Old Drugs for New Ailments: AI searches for Covid-19 treatments among existing drugs.

Many medical drugs work by modulating the body’s production of specific proteins. Recent research aimed to predict this activity, enabling researchers to identify drugs that might counteract the effects of Covid-19.
Schematic of a typical deep learning workflow
Graph Neural Networks (GNN)

(Science) Community Outreach: A survey of machine learning from Eric Schmidt

Are your scientist friends intimidated by machine learning? They might be inspired by a primer from one of the world’s premier tech titans. Former Google CEO Eric Schmidt and Cornell PhD candidate Maithra Raghu school scientists in machine learning in a sprawling overview.
Data related to model that predicts molecules that are structurally unrelated to known antibiotics
Graph Neural Networks (GNN)

Deep Learning Finds New Antibiotic: Researchers used AI to identify a promising new antibiotic.

Chemists typically develop new antibiotics by testing close chemical relatives of tried-and-true compounds like penicillin. That approach becomes less effective, though, as dangerous bacteria evolve resistance to those very chemical structures. Instead, researchers enlisted neural networks.
Information related to a model that predicts a chemical's smell
Graph Neural Networks (GNN)

Nose Job: AI predicts smell by analyzing a molecule's structure.

Predicting a molecule’s aroma is hard because slight changes in structure lead to huge shifts in perception. Good thing deep learning is developing a sense of smell.

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