activity
20162020
most citedChip Placement with Deep Reinforcement Learning

152 citations · 204 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG202017 cited

Scalable Deep Generative Modeling for Sparse Graphs

Hanjun Dai, Azade Nazi, Yujia Li +2

Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However curren…

cs.LG2020152 cited

Chip Placement with Deep Reinforcement Learning

Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan +19

In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approa…

cs.LG2019

Generalized Clustering by Learning to Optimize Expected Normalized Cuts

Azade Nazi, Will Hang, Anna Goldie +2

We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples. Our clustering objective is based on optimizing normalized cuts, a criterion wh…

cs.LG201935 cited

GAP: Generalizable Approximate Graph Partitioning Framework

Azade Nazi, Will Hang, Anna Goldie +2

Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions. Due to its combinatorial nature, ma…

cs.SI2016

Web Item Reviewing Made Easy By Leveraging Available User Feedback

Azade Nazi, Mahashweta Das, Gautam Das

The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Thou…