2 citations · 4 across the 5 of their papers we have counts for
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cs.LG2023★ 1 cited
Improving Energy Conserving Descent for Machine Learning: Theory and Practice
G. Bruno De Luca, Alice Gatti, Eva Silverstein
We develop the theory of Energy Conserving Descent (ECD) and introduce ECDSep, a gradient-based optimization algorithm able to tackle convex and non-convex optimization problems. T…
cs.LG2021
Deep Learning and Spectral Embedding for Graph Partitioning
Alice Gatti, Zhixiong Hu, Tess Smidt +2
We present a graph bisection and partitioning algorithm based on graph neural networks. For each node in the graph, the network outputs probabilities for each of the partitions. Th…
cs.LG2021★ 1 cited
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
Alice Gatti, Zhixiong Hu, Tess Smidt +2
We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser represen…