Yanshuai Cao

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Senior Research Team Lead

PhD Computer Science, University of Toronto

Yanshuai Cao is a senior research team lead at Borealis AI. Currently, his research spans natural language processing, generative models, and adversarial machine learning. He conceived the idea for project Turing by Borealis AI, a cross-domain natural language database interface, and led the research to enable it. His long-term research goal is to create machines that can learn to reason and adapt quickly from as little data as possible.

Yanshuai received his PhD in Computer Science from the Department of Computer Science at the University of Toronto, where he was advised by Professor David J. Fleet and Professor Aaron Hertzmann.

Research Areas

Natural Language Processing

Computer Vision

Publications

June 3, 2021 Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data
Association for Computational Linguistics (ACL), 2021
Authors: S. Norouzi, K. Tang , Y. Cao
Abstract Paper BibTeX
June 3, 2021 Optimizing Deeper Transformers on Small Datasets
Association for Computational Linguistics (ACL), 2021
Authors: P. Xu , D. Kumar, W. Yang , W. Zi , K. Tang , C. Huang , J. Chi Kit Cheung, S. Prince , Y. Cao
Abstract Paper BibTeX
June 3, 2021 TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
Association for Computational Linguistics (ACL), 2021
Authors: *P. Xu , *W. Zi , H. Shahidi , A. Kadar , K. Tang , W. Yang , J. Ateeq , H. Barot , M. Alon , Y. Cao
* Denotes equal contribution
Abstract Paper BibTeX
June 3, 2021 A Globally Normalized Neural Model for Semantic Parsing
Workshop at Association for Computational Linguistics (ACL), 2021
Authors: C. Huang , W. Yang , Y. Cao , O. R. Zaïane, L. Mou
Abstract Paper BibTeX
July 12, 2020 Evaluating Lossy Compression Rates of Deep Generative Models
International Conference on Machine Learning (ICML), 2020
Authors: *S. Huang, *A. Makhzani, Y. Cao , R. Grosse
* Denotes equal contribution
Abstract Paper BibTeX
July 12, 2020 On Variational Learning of Controllable Representations for Text without Supervision
International Conference on Machine Learning (ICML), 2020
Authors: P. Xu , Y. Cao
Abstract Paper BibTeX
June 3, 2020 Better Long-Range Dependency by Bootstrapping A Mutual Information Regularizer
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Authors: *Y. Cao , *P. Xu
* Denotes equal contribution
Abstract Paper BibTeX
July 28, 2019 A Cross-Domain Transferable Neural Coherence Model
Association for Computational Linguistics (ACL), 2019
Authors: P. Xu , H. Saghir, J. Kang , L. Long, A. J. Bose, Y. Cao
Abstract Paper BibTeX
Dec. 3, 2018 Few-Shot Self Reminder to Overcome Catastrophic Forgetting
Workshop on Continual Learning (NeurIPS), 2018
Authors: J. Wen, Y. Cao , R. Huang
Abstract Paper BibTeX
Dec. 3, 2018 Compositional Hard Negatives for Visual Semantic Embeddings via an Adversary
Workshop on ViGIL (NeurIPS), 2018
Authors: *A. J. Bose, *H. Ling, Y. Cao
* Denotes equal contribution
Abstract Paper BibTeX
July 15, 2018 Adversarial Contrastive Estimation
Association for Computational Linguistics (ACL), 2018
Authors: *A. J. Bose, *H. Ling, *Y. Cao
* Denotes equal contribution
Abstract Paper BibTeX Blog
April 30, 2018 Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
International Conference on Learning Representations (ICLR), 2018
Authors: Y. Cao , G. W. Ding , K. Lui , R. Huang
Abstract Paper BibTeX Blog
Aug. 6, 2017 Implicit Manifold Learning on Generative Adversarial Networks
Workshop on Implicit Models (ICML), 2017
Authors: K. Lui , Y. Cao , M. Gazeau, K. S. Zhang
Abstract Paper BibTeX Blog
Aug. 6, 2017 Automatic Selection of t-SNE Perplexity
Workshop on AutoML (ICML), 2017
Authors: Y. Cao , L. Wang
Abstract Paper BibTeX Blog
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