A Constant-Factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem - Svensson, Tarnawski et Végh -- 2017 Computer Science |
- A Constant-Factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem - Svensson, Tarnawski et Végh -- 2017
- MLIR (a Google Project) : Redefining the compiler infrastructure
- Quick question regarding Boolean algrebra.
- Currently in 2nd year Software Development. Started doing udemy courses to further enhance my understanding and now I find them much much better than my lecturers. Anyone else agree?
- How do I go about making a video game AI?
- MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (ICML 2019)
- What's the best way to take notes during lectures?
- Does a CS degree involve geometry?
| Posted: 30 Sep 2019 02:43 AM PDT |
| MLIR (a Google Project) : Redefining the compiler infrastructure Posted: 29 Sep 2019 08:00 AM PDT |
| Quick question regarding Boolean algrebra. Posted: 30 Sep 2019 03:40 AM PDT Hello, I'm an high school student currently learning the basics of boolean algebra and have a test in a few days, and I got a simple question regarding the answer to an equation. If i have the equation y = (AB+C) and A, B and C = 1, Does the answer become 1 or 2? As far as I know it should be 1 because bools can only be 1 or 0, but the equation itself would be 2. [link] [comments]  |
| Posted: 30 Sep 2019 01:45 AM PDT |
| How do I go about making a video game AI? Posted: 29 Sep 2019 11:20 AM PDT |
| Posted: 29 Sep 2019 12:41 PM PDT https://i.redd.it/0kizekvf6lp31.jpg GitHub: https://github.com/benedekrozemberczki/MixHop-and-N-GCN Paper: https://arxiv.org/pdf/1905.00067.pdf Abstract: Recent methods generalize convolutional layers from Euclidean domains to graph-structured data by approximating the eigenbasis of the graph Laplacian. The computationally-efficient and broadly-used Graph ConvNet of Kipf & Welling, over-simplifies the approximation, effectively rendering graph convolution as a neighborhood-averaging operator. This simplification restricts the model from learning delta operators, the very premise of the graph Laplacian. In this work, we propose a new Graph Convolutional layer which mixes multiple powers of the adjacency matrix, allowing it to learn delta operators. Our layer exhibits the same memory footprint and computational complexity as a GCN. We illustrate the strength of our proposed layer on both synthetic graph datasets, and on several real-world citation graphs, setting the record state-of-the-art on Pubmed. [link] [comments]  |
| What's the best way to take notes during lectures? Posted: 29 Sep 2019 09:51 AM PDT My method right now is write rough notes in my rough book, then copy these notes in my neat book and add some extra info along the way. I'm only 1 week into uni so I can't say how well this is working out just yet, but I'd be interested to see how you guys do or did it. Thing is, all my lecture slides are already online so writing them almost feels pointless, but like people say, writing them perhaps helps you to retain the information? Cheers for any feedback [link] [comments]  |
| Does a CS degree involve geometry? Posted: 29 Sep 2019 03:13 PM PDT I suck at geometry, and I dislike it so much. However, I am very good at calculus. [link] [comments]  |
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