I am a Ph.D. student at University of Toronto Machine Learning Group. My advisors are
Roger Grosse and
Geoffrey Hinton. I also closely work with
Office: 265D, D.L. Pratt Building.
My research interests are biologically-plausible learning, sequential decision making, optimization, learning theory.
Our submission to ICLR: On the Quantitative Analysis of Decoder-Based Generative Models [arxiv] is accepted as a poster presentation. Now we are able to quantitatively measure performances of GANs!
One journal paper accepted to appear in Neural Computation!
3 (co)first-authored papers accepted to appear at NIPS 2016!
On Multiplicative Integration with Recurrent Neural Networks. Yuhuai Wu*, Saizheng Zhang*, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov. NIPS, 2016. [arxiv]
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. Behnam Neyshabur*, Yuhuai Wu*, Ruslan Salakhutdinov, Nathan Srebro. NIPS, 2016. [arxiv]
Architectural Complexity Measures of Recurrent Neural Networks. Saizheng Zhang*, Yuhuai Wu*, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio. NIPS, 2016. [arxiv]
Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI. Geoffrey Roeder, Yuhuai Wu, David Duvenaud. NIPS 2016 workshop in Advances in Approximate Bayesian Inference. [workshop]
I am/was a TA for
CSC 321 : Introduction to Neural Networks (2017 spring)
ECE 521 : Inference Algorithms and Machine Learning (2017 spring)
CSC 236: Introduction to the Theory of Computation (2016 summer)
CSC 148: Introduction to Computer Science (2016 spring)
CSC 165: Mathematical Expression and Reasoning for Computer Science (2015 fall)
On the Quantitative Analysis of Decoder-Based Generative Models. NIPS workshop in Adversarial training. 2016/12.
On the Quantitative Analysis of Decoder-Based Generative Models. OpenAI. 2016/11.
Architectural Complexity Measures & Multiplicative Integration of RNNs. U of Toronto. 2016/10.
Intro to Differential Geometry. U of Toronto. 2016/07.
Architectural Complexity Measures of Recurrent Neural Networks. Toyota Technological Institute at Chicago. 2016/04.