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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.

Learning a Deep ConvNet for Multi-label Classification with Partial Labels

Authors: T. Durand , N. Mehrasa , G. Mori

On the Sensitivity of Adversarial Robustness to Input Data Distributions

Authors: G. Weiguang Ding , K. Lui , X. 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

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

Authors: Y. Cao , G. W. Ding , K. Lui , R. Huang

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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