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10/07/2022
Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR
Large neural network models are commonly trained through a combination o...
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09/26/2022
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (“DFT”) force...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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12/06/2021
Automap: Towards Ergonomic Automated Parallelism for ML Models
The rapid rise in demand for training large neural network architectures...
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06/15/2021
Very Deep Graph Neural Networks Via Noise Regularisation
Graph Neural Networks (GNNs) perform learned message passing over an inp...
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09/16/2019
Learning Index Selection with Structured Action Spaces
Configuration spaces for computer systems can be challenging for traditi...
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09/15/2019
Wield: Systematic Reinforcement Learning With Progressive Randomization
Reinforcement learning frameworks have introduced abstractions to implem...
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10/21/2018
RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning
Reinforcement learning (RL) tasks are challenging to implement, execute ...
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08/23/2018