Search Results for author: Azade Nazi

Found 5 papers, 3 papers with code

GAP: Generalizable Approximate Graph Partitioning Framework

1 code implementation2 Mar 2019 Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini

Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions.

Clustering graph partitioning

Generalized Clustering by Learning to Optimize Expected Normalized Cuts

no code implementations16 Oct 2019 Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini

We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples.

Clustering

Scalable Deep Generative Modeling for Sparse Graphs

1 code implementation ICML 2020 Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans

Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\log n)$.

Graph Generation

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