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Data Scientology

Data Scientology

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Channel Posts
Data Scientology
Conference papers are cooked! D Got three papers to review for ICLR and five for AAMAS, and believe me the quality of these papers is insanely bad. I would even refrain from accepting them to a workshop, let alone be main venues. A sincere request; Please don't submit the paper unless it is ready and if you are a first timer, please please get a feedback from a mentor out. Otherwise you are just wasting the time of so many reviewers out there https://redd.it/1x9fyhd @datascientology

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Detecting sewer pipe defects in CCTV inspection video: anyone working on something similar? https://redd.it/1x6tzsq @datascie
Detecting sewer pipe defects in CCTV inspection video: anyone working on something similar? https://redd.it/1x6tzsq @datascientology
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PhD students, how ya feeling? D All seems a bit pointless now. Either pump out AI papers and neglect your own development, or take it slow and fall behind in the publishing race. I already have two papers made largely without LLMs (still seeking publication), but it's hard to resist just vibing the third from start to finish with Claude. https://redd.it/1x5fv1a @datascientology
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Is this Gaussian Splatting, GenAI or some other technique? https://redd.it/1x3tvpw @datascientology
Is this Gaussian Splatting, GenAI or some other technique? https://redd.it/1x3tvpw @datascientology
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Nvidia’s erroneous paper accepted as ICML’s spotlight D Here is the story: Nvidia has published this work (with source code available) called dreamDojo which is a world model for robotics based of their prior work Cosmos 2.5 which is cited about 100 times and got ICML’s spotlight. Authors are very well known and respected in the field with too many peer reviewed papers already published. The work doesn’t have much novelty (which I don’t care) but it is yet another foundation model. The gist is that they collected about 44k hours of human data (data are NOT open sourced, which I don’t care) and used that for pre-training of DreamDojo initialized from Cosmos 2.5 . Table 4 of the paper though shows a very marginal improvement over cosmos 2.5. Believe or not, just about 0.5 db PSNR improvement. Its fishy, is not it? You use 44000 hours of human data and few hundred hours of other kind of data and robot data and 256 H100 gpu and you got just a very marginal improvement. Anyway I went to give it a try, started post training on their GR1 released data, and could produce their results. Meanwhile a colleague with the help of Claude found a bug in their post training code, which essentially make all the post training code wrong. Then we checked their github issues, and noticed that two other bugs are reported which effect the whole pre-training! Especially this bug . So essentially based on the code their released everything in pre training phase, post training phase and evaluation is buggy and is wrong. The code doesn’t seem AI written cause its (very) bad written. And now after seeing all those bugs the results in paper makes sense. My question is: WTF! How it did not ring a bell for the authors. Did not they ask we spend a hell lot of resources and huge amount of data and essentially got nothing? Did not it ring a bell for reviewers? Openreview https://redd.it/1x0i6b5 @datascientology
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Saw this on Rednote, WTF D https://preview.redd.it/t7wxguxqvzth1.png?width=1314&format=png&auto=webp&s=0ba382fb88894c6e687d5bb9a4b14a85334eae67 Guess we have a dataset for sycophancy and AI content detection https://redd.it/1wzpzh3 @datascientology
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Distilling Stockfish on a Billion Positions, Full 3.9B Dataset Available [P] https://blog.lukesalamone.com/posts/distilling-stockfish https://redd.it/1wxz5qq @datascientology
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arXiv now limits submitters to up to two submissions per calendar month [N] https://blog.arxiv.org/2026/10/01/updated-rate-limit-policy/ https://redd.it/1wvg7yc @datascientology
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LessThink-Qwen3-4B: the same model, with far less thinking P I post-trained Qwen3-4B to spend 44% fewer tokens on reasoning, keeping its knowledge and answer style. The whole pipeline ran on one GPU. folks, you can check it out on : https://5ivatej.com/lessthink/ https://redd.it/1wtygav @datascientology
125
10
Free, open-source AI engineering course where you build each algorithm by hand: 523 lessons, now as EPUB/PDF books P AI Engineering from Scratch is an MIT-licensed curriculum: 523 lessons across 20 phases, from linear algebra and backprop to transformers, LLMs, agents, and production serving. The code is stdlib-first, so you see every step instead of calling a library. This month's edition: \- six EPUB and PDF volumes built from the lessons, attached to the release \- the site interface and lessons in eight languages (Chinese, Hindi, Spanish, Arabic, French, Portuguese, Turkish, Vietnamese) \- CI now runs each lesson's own tests, and a sweep fixed datasets, models, and links that had stopped working Site: https://aiengineeringfromscratch.com Books and release notes: https://github.com/rohitg00/ai-engineering-from-scratch/releases/tag/v2026.10 If you use a coding agent, npx skills add rohitg00/ai-engineering-from-scratch and then /start-learning gives you a placement quiz and a study plan. https://redd.it/1ws6e9p @datascientology
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11
I made a computer vision tool for rock climbing analysis in 3D using iPhone LiDAR! https://redd.it/1wphdyq @datascientology
I made a computer vision tool for rock climbing analysis in 3D using iPhone LiDAR! https://redd.it/1wphdyq @datascientology
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Penalty kick analysis running fully in the browser at ~30 fps with WebGPU https://redd.it/1wnafi6 @datascientology
Penalty kick analysis running fully in the browser at ~30 fps with WebGPU https://redd.it/1wnafi6 @datascientology
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No text...
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[Upcoming AMA] Waymo AI Team AMA – Drop Your Questions Early! [D] https://redd.it/1wfesc0 @datascientology
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I made a computer vision tool for running analysis! https://redd.it/1wcx27z @datascientology
I made a computer vision tool for running analysis! https://redd.it/1wcx27z @datascientology
367
16
OpenAl Says It Has Cracked One of Math's “Millennium Problems” (Navier-Stokes) N As reported by the New York Times: https://www.nytimes.com/2026/09/08/science/openai-proof-millennium-problem.html?smid=nytcore-ios-share OpenAI’s announcement: https://openai.com/index/navier-stokes-solution/ https://redd.it/1wavdi7 @datascientology
285
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Camera recommendation for real-time object tracking on a conveyor belt (Budget: ~$150 - $280) https://redd.it/1w9nud3 @datasc
Camera recommendation for real-time object tracking on a conveyor belt (Budget: ~$150 - $280) https://redd.it/1w9nud3 @datascientology
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18
World Labs' new Atlas model: Space-time simulation, "bullet time" from 3 cell phones, and scalable Real-to-Sim https://redd.i
World Labs' new Atlas model: Space-time simulation, "bullet time" from 3 cell phones, and scalable Real-to-Sim https://redd.it/1w4m5yr @datascientology
386
19
Where to submit stat/prob ML D I'm a researcher in statistical and probabilistic ML, I have a steady record of top ML publications and really used to enjoy going to conferences. Over the last few years LLM based works have completely taken over the top conferences. At this year's ICLR, walking among the rows of posters you were lucky to find one paper per row of 10 that wasn't about how their favourite LLM could or couldn't solve their niche benchmark. The workshops tell the same story, most are some kind of agentic flavour. Looking at this year's NeurIPS workshops it's the same thing, basically all are about agents. I'm wondering where do the stat/prob ML communities go from here? I look up to people like Arnaud Doucet, Aapo Hyvärinen, Christian Naesseth, Stefano Ermon, they seem to still publish at the top 3? On my end, I m thinking AISTATS/UAI might be the way to go. All in all, the top 3 might never really have been intended as the home for prob/statML works, it just happened to be the 'prestigious' venue. https://redd.it/1w0kipf @datascientology
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Qwen 3.6 VLM playing “Where’s Waldo?” https://redd.it/1vylz7a @datascientology
Qwen 3.6 VLM playing “Where’s Waldo?” https://redd.it/1vylz7a @datascientology
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