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Found 7 papers, 5 papers with code
Spherical Channels for Modeling Atomic Interactions
We propose the Spherical Channel Network (SCN) to model atomic energies and forces.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis
The dataset and baseline models are open sourced, and a public leaderboard will follow to encourage continued community developments on the total energy tasks and data.
Uncovering the impact of learning rate for global magnitude pruning
However, prior work has implicitly assumed that the best training configuration for model performance was also the best configuration for mask discovery.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
First-Order Preconditioning via Hypergradient Descent
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space. These difficulties can be addressed by second-order approaches that apply a pre-conditioning matrix to the gradient to improve convergence.
LCA: Loss Change Allocation for Neural Network Training
We propose a new window into training called Loss Change Allocation (LCA), in which credit for changes to the network loss is conservatively partitioned to the parameters.
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights.
