activity
20132019
most citedA Method Based on Total Variation for Network Modularity Optimization using the MBO Scheme

4 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ML2019

Deep Ensembles: A Loss Landscape Perspective

Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan

Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ens…

cs.CV2019

Cross-View Policy Learning for Street Navigation

Ang Li, Huiyi Hu, Piotr Mirowski +1

The ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL).…

cs.LG2018

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems

Timothy A. Mann, Sven Gowal, András György +4

Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed on…

cs.CV20171 cited

End-to-End Interpretation of the French Street Name Signs Dataset

Raymond Smith, Chunhui Gu, Dar-Shyang Lee +5

We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France. Each image…

cs.SI20134 cited

A Method Based on Total Variation for Network Modularity Optimization using the MBO Scheme

Huiyi Hu, Thomas Laurent, Mason A. Porter +1

The study of network structure is pervasive in sociology, biology, computer science, and many other disciplines. One of the most important areas of network science is the algorithm…