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7 papers · 2 filters
GDPP: Learning Diverse Generations Using Determinantal Point Process
Mohamed Elfeki, Camille Couprie, Morgane Riviere +1
Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images. An essential characteri…
Differentiable Greedy Networks
Thomas Powers, Rasool Fakoor, Siamak Shakeri +4
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is t…
Learning to Perform Local Rewriting for Combinatorial Optimization
Xinyun Chen, Yuandong Tian
Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this pap…
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
Ji Wang, Jianguo Zhang, Weidong Bao +3
The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cl…
Backplay: "Man muss immer umkehren"
Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor +3
Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the…
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li +3
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding…