8 citations · 14 across the 9 of their papers we have counts for
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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.AI2023
MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder
Guoliang He, Zak Singh, Eiko Yoneki
Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequenc…
cs.SI2023
Guided Graph Generation: Evaluation of Graph Generators in Terms of Network Statistics, and a New Algorithm
Jérôme Kunegis, Jun Sun, Eiko Yoneki
We consider the problem of graph generation guided by network statistics, i.e., the generation of graphs which have given values of various numerical measures that characterize net…