If you build or maintain software, you’re familiar with GitHub. Millions of developers rely on the massive code repository for everything from source code
While implementing a quick toy example of Crane and Sawhney's really great Monte Carlo Geometry Processing paper, the question arose about whether a quick function I grabbed from The Internet to equally distribute points on a sphere was correct or not. Since it's absolutely the crux of the method, this is an important question! This notebook performs a rather unscientific check for equal distribution of points on the surface of a sphere. It uses the first algorithm from MathWorld: Sphere Point Picking. Foll
It is a live weekly hour-long webseries showcasing geometry processing research. Topics range from computer science, mathematics, and engineering including 3D deep learning, computational fabrication, and computer graphics. The unique format of the Toronto Geometry Colloquium pairs a 10-min opener speaking about a recent work with a 50-min headliner giving a keynote-style address
Beginning November 13th, 2020, we will no longer accept account passwords when authenticating with the GitHub REST API. In the future, we will similarly no longer accept account passwords when authenticating Git operations.
Let’s imagine a hypothetical situation. There’s an infection going round, and we want to predict the future severity of someone’s illness. There is a test that offers a good prediction. Let’s say the outcome of the test has a correlation of 0.78 with the patient's severity of infection. The problem with the test is that…
This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
- Aug. 31 – Sep. 4, 2020
- Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)
This page contains list of mathematical Theorems which are at the same time (a) great, (b) easy to understand, and (c) published in the 21st century. See here for more details about these criteria. Click on any theorem to see the exact formulation, or click here for the formulations of all theorems. You can also…
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`.
Every minute, South Korea's household debt rises by US$90 thousand dollars. Every 12 minutes, a Korean is declared bankrupt. Ordinary households now owe some...
- Aug. 19 – Aug. 28, 2020
- Nike Sun (Massachusetts Institute of Technology; chair), Jian Ding (University of Pennsylvania), Ronen Eldan (Weizmann Institute), Elchanan Mossel (Massachusetts Institute of Technology), Joe Neeman (University of Texas at Austin), Jelani Nelson (UC Berkeley), Tselil Schramm (Stanford University; Microsoft Research Fellow)
H. Tajima, and F. Fujisawa. (2020)cite arxiv:2007.00926Comment: 6 pages, 5 figures, accepted by Scientific and Educational Reports of the Faculty of Science and Technology, Kochi University.
R. Hanocka, G. Metzer, R. Giryes, and D. Cohen-Or. (2020)cite arxiv:2005.11084Comment: SIGGRAPH 2020; Project page: https://ranahanocka.github.io/point2mesh/.