6 citations · 6 across the 1 of their papers we have counts for
3 papers
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen +11
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorit…
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks
Frank Schneider, Felix Dangel, Philipp Hennig
When engineers train deep learning models, they are very much 'flying blind'. Commonly used methods for real-time training diagnostics, such as monitoring the train/test loss, are…
DeepOBS: A Deep Learning Optimizer Benchmark Suite
Frank Schneider, Lukas Balles, Philipp Hennig
Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area. Yet,…