8 citations · 14 across the 9 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2023★ 2 cited
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
Guoliang He, Sean Parker, Eiko Yoneki
Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure. Such a graph transformation…
cs.LG2021★ 2 cited
BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters
Sami Alabed, Eiko Yoneki
Current auto-tuning frameworks struggle with tuning computer systems configurations due to their large parameter space, complex interdependencies, and high evaluation cost. Utilizi…
cs.LG2016★ 8 cited
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection
Michael Schaarschmidt, Felix Gessert, Valentin Dalibard +1
Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforce…