CGLab (since 2016.09) focuses on conducting research on photorealistic rendering, which includes a variety of optimization techniques for ray tracing. The main applications of photorealistic rendering are CG movies, animations, 3D games and immersive technology (AR and VR).
Military hierarchies are, by necessity, rigid structures. DARPA’s ‘Mosaic Warfare’ project aims for something much more fluid and adaptable, with AI doing the logistical grunt work so human commanders can get creative.
- 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…
GPUs are designed to do many things well, but drawing transparent 3D objects is not one of them. Opacity doesn't commute so that the order in which you draw surfaces makes a big difference. Of course simple additive blending does commute, but it's not really what we think of as "transparent objects". The simplest way to draw transparent objects is from back to front via the painter's algorithm. In this approach we sort geometry and draw only from back to front. This requires sorting triangles, which, in add
This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
These are articles about the techniques I develop and lessons I learnt while toying or working with computer graphics. Most of it is self-taught and there's lots of reinventing the wheel (which I recommend) but also some innovative and new discoveries that often times are not documented anywhere else (and if any of this content becomes part of your paper or the center of your PhD thesis, I feel it'd be fair to mention this website).