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Found 13 papers, 4 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.
Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR
Large neural network models are commonly trained through a combination of advanced parallelism strategies in a single program, multiple data (SPMD) paradigm.
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery.
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks.
Automap: Towards Ergonomic Automated Parallelism for ML Models
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline parallelism.
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
We introduce “Noisy Nodes”, a very simple technique for improved training of GNNs, in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Initial Structure to Relaxed Energy (IS2RE), Direct
Molecular Property Prediction
+1
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
From this observation we derive "Noisy Nodes", a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Ranked #4 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
Learning Index Selection with Structured Action Spaces
Configuration spaces for computer systems can be challenging for traditional and automatic tuning strategies.
Wield: Systematic Reinforcement Learning With Progressive Randomization
Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale.
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns.
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks.
Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization
We present an optimizer which uses Bayesian optimization to tune the system parameters of distributed stochastic gradient descent (SGD).
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection
This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases under latency constraints.
