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Found 7 papers, 3 papers with code
PartIR: Composing SPMD Partitioning Strategies for Machine Learning
Training of modern large neural networks (NN) requires a combination of parallelization strategies encompassing data, model, or optimizer sharding.
Decomposing reverse-mode automatic differentiation
We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition.
Automatically Batching Control-Intensive Programs for Modern Accelerators
We present a general approach to batching arbitrary computations for accelerators such as GPUs.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on graphs.
Ranked #2 on
Drug Discovery
on HIV dataset
Early Stopping is Nonparametric Variational Inference
By tracking the change in entropy over this sequence of transformations during optimization, we form a scalable, unbiased estimate of the variational lower bound on the log marginal likelihood.
Gradient-based Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable.
Firefly Monte Carlo: Exact MCMC with Subsets of Data
Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for Bayesian inference.
