3 citations · 7 across the 8 of their papers we have counts for
3 papers · 1 filter
What makes a good feedforward computational graph?
Alex Vitvitskyi, João G. M. Araújo, Marc Lackenby +1
As implied by the plethora of literature on graph rewiring, the choice of computational graph employed by a neural network can make a significant impact on its downstream performan…
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…
Scalable Training of Language Models using JAX pjit and TPUv4
Joanna Yoo, Kuba Perlin, Siddhartha Rao Kamalakara +1
Modern large language models require distributed training strategies due to their size. The challenges of efficiently and robustly training them are met with rapid developments on…