73 citations · 197 across the 28 of their papers we have counts for
3 papers · 1 filter
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models
Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal +1
Neural architectures can be naturally viewed as computational graphs. Motivated by this perspective, we, in this paper, study neural architecture search (NAS) through the lens of l…
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
Hierarchical Maximum-Margin Clustering
Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt +2
We present a hierarchical maximum-margin clustering method for unsupervised data analysis. Our method extends beyond flat maximum-margin clustering, and performs clustering recursi…