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Academic Freedom.
Computational Power.
Massive Datasets.
Curiosity-driven research dedicated to achieving
state-of-the-art in machine learning and artificial intelligence.

Publications

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Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Authors: T. Durand , N. Mehrasa , G. Mori

A Variational Auto-Encoder Model for Stochastic Point Process
Authors: N. Mehrasa , A. Jyothi , T. Durand , J. He , Prof. L. Sigal , G. Mori

On the Sensitivity of Adversarial Robustness to Input Data Distributions
Authors: G. Weiguang Ding , K. Lui , T. Jin , L. Wang, R. Huang

Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
Authors: K. Lui , G. W. Ding , R. Huang , R.J. McCann

Improving Reinforcement Learning with Human Input
Authors: M. E. Taylor

Adversarial Contrastive Estimation
Authors: *A. J. Bose, *H. Ling, *Y. Cao
* Denotes equal contribution
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Updates

Meet the Class of 2019: Introducing Borealis AI’s Graduate Fellowship winners
Blog

Everything old is new again: Tackling classical problems with modern techniques at AAAI 2019
Blog

Kathryn Hume joins Borealis AI leadership team
Blog

How to Set Up an AI R&D Lab
Press

The Pommerman team competition or: How we learned to stop worrying and love the battle
Blog

Northern Frontier: In conversation with Abhishek Gupta
Video