Artificial intelligence makes blurry faces look more than 60 times sharper: This AI turns even the blurriest photo into realistic computer-generated faces in HD Computer Science |
- Artificial intelligence makes blurry faces look more than 60 times sharper: This AI turns even the blurriest photo into realistic computer-generated faces in HD
- Examples of Heuristics in Computer Science
- Very difficult cuboid problem.
- Scientists create the most efficient algorithm for the Traveling Salesman Problem (TSP) to date
- Can Comma AI drive backwards or can it only see in front of the car?
- Understanding numerical errors in computation
- What is using more data/ressource : peer to peer or server-client ?
| Posted: 29 Nov 2020 08:14 AM PST |
| Examples of Heuristics in Computer Science Posted: 30 Nov 2020 05:41 AM PST |
| Very difficult cuboid problem. Posted: 30 Nov 2020 04:00 AM PST hey guys You have your big cuboid C, located ona 3d grid, some smaller cuboids (c,c1,c2) inside, and want to count the number of subcuboids of C which do not properly intersect with (c,c1,c2) how to compute this? do you have any ideas? [link] [comments]  |
| Scientists create the most efficient algorithm for the Traveling Salesman Problem (TSP) to date Posted: 29 Nov 2020 02:48 PM PST |
| Can Comma AI drive backwards or can it only see in front of the car? Posted: 29 Nov 2020 01:37 PM PST Can Comma AI drive backwards or can it only see in front of the car? [link] [comments]  |
| Understanding numerical errors in computation Posted: 29 Nov 2020 07:27 AM PST I am learning numerical methods from "Numerical Methods for Engineers", 7th Edition, Chapra and Canale. I am having a hard time understanding the explanation given there for chopping and rounding errors applied to binary systems(Page 71 Chapter 3). For base-10 it is ok for me. This is what the authors say and I quote for chopping errors,"Note that for the base-2 number system in Fig. 3.9, chopping means that any quantity falling within an interval of length delta_x will be stored as the quantity at the lower end of the interval. Thus, the upper error bound for chopping is delta_x. Additionally, a bias is introduced because all errors are positive." For rounding the authors explain and I quote,"Note that for the base-2 number system in Fig. 3.9, rounding means that any quantity falling within an interval of length delta_x will be represented as the nearest allowable number. Thus, the upper error bound for rounding is delta_x/2. Additionally, no bias is introduced because some errors are positive and some are negative." This is the figure 3.9 they are referring to and it was generated based on this example. I can also post the full example if needed. Can someone please explain what these two explanations mean? Understanding of these two paragraphs are important because the authors use these explanations to illustrate the concept of machine epsilon. [link] [comments]  |
| What is using more data/ressource : peer to peer or server-client ? Posted: 29 Nov 2020 11:25 AM PST |
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