649 citations
- Carnegie Mellon UniversityUS26 papers
- Stanford UniversityUS20 papers
- Google (United States)US14 papers
- Georgia Institute of TechnologyUS13 papers
- Tel Aviv UniversityIL12 papers
- Cornell UniversityUS11 papers
- Meta (United States)US11 papers
- University of California, BerkeleyUS11 papers
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- Johns Hopkins UniversityUS9 papers
- Massachusetts Institute of TechnologyUS9 papers
22 papers · 2 filters
Predictive Precompute with Recurrent Neural Networks
Hanson Wang, Zehui Wang, Yuanyuan Ma
In both mobile and web applications, speeding up user interface response times can often lead to significant improvements in user engagement. A common technique to improve responsi…
AR-Net: A simple Auto-Regressive Neural Network for time-series
Oskar Triebe, Nikolay Laptev, Ram Rajagopal
In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-ra…
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies
Laurent Meunier, Jamal Atif, Olivier Teytaud
We introduce a new black-box attack achieving state of the art performances. Our approach is based on a new objective function, borrowing ideas from -white box attacks…
Live Face De-Identification in Video
Oran Gafni, Lior Wolf, Yaniv Taigman
We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having…
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Samuel Daulton, Shaun Singh, Vashist Avadhanula +2
Recent advances in contextual bandit optimization and reinforcement learning have garnered interest in applying these methods to real-world sequential decision making problems. Rea…
Hyperbolic Graph Neural Networks
Qi Liu, Maximilian Nickel, Douwe Kiela
Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated…