[Explainable AI] Interpret complex neural network's decisions with simple linear regressions Computer Science

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• [Explainable AI] Interpret complex neural network's decisions with simple linear regressions
• Don't really understand the appeal of splay trees
• 'Stupendously large' black holes could grow to truly monstrous sizes
• [N] The Eyes Have It: 20 Million Images Make TEyeD World’s Largest Human Eye Dataset
• ML Research? Yes? No?

[Explainable AI] Interpret complex neural network's decisions with simple linear regressions Posted: 09 Feb 2021 12:59 AM PST submitted by /u/longuyen2306 [link] [comments]

Don't really understand the appeal of splay trees Posted: 08 Feb 2021 02:03 PM PST So when we access an item, we splay that item (make it the root node) so that item can be easily accessed in the future? Isn't that sort of wishful thinking that just because we accessed this node once, we're going to do it again soon? I don't understand submitted by /u/Missing_Back [link] [comments]

'Stupendously large' black holes could grow to truly monstrous sizes Posted: 08 Feb 2021 10:46 PM PST submitted by /u/Groundbreaking-Net73 [link] [comments]

[N] The Eyes Have It: 20 Million Images Make TEyeD World’s Largest Human Eye Dataset Posted: 08 Feb 2021 06:46 PM PST A trio of researchers from Germany's University Tübingen have introduced a unified dataset of over 20 million human eye images captured using seven different head-mounted eye trackers. Dubbed TEyeD, the dataset is the largest of its kind and includes 2D and 3D annotations on eye movement types. Landmarks and semantic segmentations are provided for the pupils, irises and eyelids to enable shift-invariant gaze estimation. Here is a quick read: The Eyes Have It: 20 Million Images Make TEyeD World's Largest Human Eye Dataset The paper TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types is available on arXiv. submitted by /u/Yuqing7 [link] [comments]

ML Research? Yes? No? Posted: 08 Feb 2021 04:13 PM PST I'm a 3rd year CS student. Average grades (but definitely do MUCH better in practical classes than in theory). Never touched ML in my life (only AI, like agents, searches, etc). Got offered a research position in ML to essentially learn ML and then develop an individual project over the course of 6 months +/-. The thing is, this semester I've been dying inside with the amount of studying we have to do. Next semester (in which the position would start) will be supposedly easier than this one. But still, I don't know if I can conjugate uni + research. Pros: - Experience on a trendy area - Possible advantage in the job market (I don't want PhD, only masters) - I'll make some money Cons: - Difficult area for a newcomer (I think) - Shit ton of work (the teachers made it VERY clear) - With these cons I'm worrying about my ability to handle all this. What do you say? Yes? No? Why? Thank you. submitted by /u/wankmore [link] [comments]

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