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Found 10 papers, 6 papers with code
GenCast: Diffusion-based ensemble forecasting for medium-range weather
Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use.
Generating QM1B with PySCF$_{\text{IPU}}$
Similar benefits are yet to be unlocked for quantum chemistry, where the potential of deep learning is constrained by comparatively small datasets with 100k to 20M training examples.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
PopSparse: Accelerated block sparse matrix multiplication on IPU
Furthermore, static sparsity in general outperforms dynamic sparsity.
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction.
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task.
8-bit Numerical Formats for Deep Neural Networks
Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efficiency of model training.
Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training
Much recent research has been dedicated to improving the efficiency of training and inference for image classification.
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence
We investigate the reasons for the performance degradation incurred with batch-independent normalization.
Revisiting Small Batch Training for Deep Neural Networks
Modern deep neural network training is typically based on mini-batch stochastic gradient optimization.
