Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most…
- Sep. 28 – Oct. 2, 2020
- Lihong Li (Google Brain; chair), Marc G. Bellemare (Google Brain)
- The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.
TL;DR: Have you even wondered what is so special about convolution? In this post, I derive the convolution from first principles and show that it naturally emerges from translational symmetry. During…
Have you ever wondered how will the machine learning frameworks of the '20s look like? In this essay, I examine the directions AI research might take and the requirements they impose on the tools at our disposal, concluding with an overview of what I believe to be the two strong candidates: `JAX` and `S4TF`.
H. Chawla, M. Jukola, T. Brouns, E. Arani, and B. Zonooz. (2020)cite arxiv:2007.12918Comment: Accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
M. Cook, A. Zare, and P. Gader. (2020)cite arxiv:2007.01263Comment: 6 pages, 4 figures, Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.