I am a computer scientist and mathematician from Salt Lake City, Utah. I’m currently doing my PhD in deep learning theory and deep reinforcement learning at Carnegie Mellon University under Ruslan Salakhutdinov. A large amount of my time is spent as CTO at Infoplay AI, a cryptocurrency hedgefund I cofounded that uses AI to interact with financial markets. [bio]
My goal is to understand human intelligence by creating artificial intelligence.
On Characterizing the Capacity of Neural Networks using Algebraic Topology. William H. Guss, Ruslan Salakhutdinov. Preprint. NIPS 2017, DLTP Workshop. [arxiv] [poster]
Towards Neural Homology Theory. William H. Guss, Ruslan Salakhutdinov. Talk, Microsoft Research, 2018. [slides]
Eigen: A Step Towards Conversational AI. William H. Guss, James Bartlett, Phillip Kuznetsov, Piyush Patil. Alexa Prize Proceedings 2017. [proceedings]
Deep Function Machines: Generalized Neural Networks for Topological Layer Expression. William H. Guss. Preprint. [arXiv]
Universal Approximation of Nonlinear Operators on Banach Space. William H. Guss. Machine Learning at Berkeley Research Symposium 2016. [pdf]
Backpropagation-Free Parallel Deep Reinforcement Learning. William H. Guss. James Bartlett, Noah Golmant, Phillip Kuznetsov, Max Johansen. Preprint (WIP). [pdf]
Parameter Reduction using Operator Neural Networks. William H. Guss. Microsoft Research Symposium 2016. Best Poster Award. [poster]
Functional Neural Networks Evaluated by Weierstrass Polynomials. William H. Guss, Phillip Kuznetsov, Patrick Chen. Intel ISEF 2015. Pittsburgh, Pennsylvania. [AAAI’ Honorable Mention] [ASA’ Honorable Mention]