Non-determinism from GPUs dominates ResNet training accuracy variability.
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A new neural network model enhances adversarial robustness without sacrificing task performance.
Can neural networks learn to solve partial differential equations (PDEs)? We investigate this question for two (systems of) PDEs, namely, the Poisson equation and the steady Navier--Stokes equations. The contributions of this paper are five-fold. (1) Numerical experiments show that small neural networks (< 500 learnabl…
Batch normalization with regularization turns deterministic autoencoders into generative models.
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state…
GRAIN: Group Aggregation via Min-Norm Objective