I just got an email from CVPR saying "For CVPR 2026, all authors are required to have a complete OpenReview profile and a complete author enrollment." But I don't understand. What is the meaning of "Complete OpenReview Profile"? I went through tens of reviews and submissions this year, and suddenly it is incomplete? Anyone has an idea about this?? submitted by /u/Public_Courage_7541 [link] [comments]

Hello everyone, I’m planning to write a literature survey paper in my research field, covering roughly the last 10–15 years of work. My goal is to submit it to TPAMI, since it’s a well-known and reputable journal that also accepts surveys. However, I’ve heard from colleagues that TPAMI sometimes considers the author’s research credentials and experience before even sending a paper for review. I’ve been working in this area for about 6 years (including 4 years during my PhD). My co-author also has some experience, but not a very strong profile. So my questions are: 1. Should I still go ahead and submit the survey to TPAMI? 2. What are my realistic odds of it being reviewed or accepted? 3. Any practical tips for writing and submitting a survey to such a high-impact journal? Thanks for your time and advice! submitted by /u/Outrageous_Tip_8109 [link] [comments]

Change in policy: Attendance for authors of accepted papers is optional. After acceptance notifications, the authors will be able to decide by a specified date whether they wish to present their paper in person at the conference or they just wish to include their paper in the proceedings (without presentation at the conference). Regardless of this choice, all the accepted papers will receive equivalent treatment in the proceedings. They will all be eligible for ICML awards as well as for the designations of distinction corresponding to the past “oral presentations” and “spotlight posters.” For proceedings-only papers, at least one of the authors must obtain virtual registration. source: https://icml.cc/Conferences/2026/CallForPapers submitted by /u/Striking-Warning9533 [link] [comments]

Hey all, I'm working on a project involving natural language search on large collections of unstructured cookbooks, with the goal of returning complete, unmodified recipes (not summaries). Example: User uploads 100 unstructured cookbooks (each containing many recipes), searches "paella," and gets 40 exact recipes returned (unmodified from the source). RAG isn’t a particularly good fit for this problem since I don’t want to re-generate/summarize the output content, I want to return exact recipes (and potentially a large volume of them). To me, I see two potential approaches: Precise chunking at index time: find out a way to accurately chunk cookbooks based on exact recipe boundaries (start/ends), and then just perform IR instead of RAG. I've tested semantic clustering and other chunking techniques, but achieving precise recipe start/end detection seems to be quite error-prone. NER feels too granular since I'm not extracting entities, just boundaries but maybe I’m wrong here. Better retrieval with post-processing: perhaps keep simpler/dumber chunking techniques and then use some sort of re-ranker/LLM to take revelant chunks from the semantic search and then “find” the beginning of the recipe passage from there, and then we can just query the original text. Wondering if anyone faced a similar problem before and any resources/techniques that would be interesting to try here. Cheers! submitted by /u/SwimmingMeringue9415 [link] [comments]

TabPFN-2.5, a pretrained transformer that delivers SOTA predictions on tabular data without hyperparameter tuning is now available. It builds on TabPFN v2 that was released in the Nature journal earlier this year. Key highlights: 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2) SOTA performance: Achieves state-of-the-art results across classification and regression Rebuilt API: New REST interface & Python SDK with dedicated fit & predict endpoints, making deployment and integration significantly more developer-friendly Want to try it out? TabPFN-2.5 is available via an API and via a package on Hugging Face. We welcome your feedback and discussion! You can also join the discord here. submitted by /u/rsesrsfh [link] [comments]

We're releasing TabPFN-2.5, a pretrained transformer that delivers SOTA predictions on tabular data without hyperparameter tuning. It builds on v2 that was released in the Nature journal earlier this year. Key highlights: 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2) SOTA performance: Achieves state-of-the-art results across classification and regression Rebuilt API: New REST interface & Python SDK with dedicated fit & predict endpoints, making deployment and integration significantly more developer-friendly Speed Boost: Delivers top performance in seconds over API Want to try it out? TabPFN-2.5 is available via API and via Hugging Face. submitted by /u/rsesrsfh [link] [comments]

I wrote a deep-dive on Kosmos after seeing lots of hype about "autonomous scientific discovery." The honest assessment: it's research acceleration, not autonomy. • 79.4% accuracy (20.6% failure rate matters) • 42,000 lines of code through iterative refinement • Reviews 1,500 papers via semantic search • But verification is still fully human-bound https://rewire.it/blog/kosmos-12-hour-ai-research-session/ submitted by /u/Fair-Rain3366 [link] [comments]

I am being asked to organize the portfolio of AI products being developed, and not sure of the best path forward. Does your leadership see AI investment like this, or in a different way? Serious answers only please. Source: https://devnavigator.com/2025/10/20/ai-investment-portfolio-matrix-balancing-innovation-impact-and-feasibility/ submitted by /u/WarChampion90 [link] [comments]

Hey Folks, I'm reading https://arxiv.org/abs/2202.08906 paper and I'm not super clear whether the ST-MOE-32B is encoder-decoder model or decoder only model. Based on the token trace detailed for encoder and decoder experts separately in section 7, I believe it is encoder-decoder, but would like to confirm with someone who has worked on it. Please let me know if I misunderstood something here. Thanks submitted by /u/red_dhinesh_it [link] [comments]

Hello. For the people here who have taught an undergraduate deep learning course, what's your favorite textbook that you have used and why? Leaning towards the Chris Murphy textbook just based on familiarity with Pattern Recognition and ML text but would love to hear what people have used before. submitted by /u/stabmasterarson213 [link] [comments]

Hey all, I’m working on a project that involves taking large sets of unstructured text (mostly books or book series) and ingesting them into a knowledge graph that can be traversed in novel ways. Ideally the structure of the graph should encode crucial relationships between characters, places, events and any other named entities. I’ve tried using various spaCy models and strict regular expression rule based parsing, but I wasn’t able to extract as complete a picture as I wanted. At this point, the only thing I can think of is using a LLM to generate the triplets used to create the graph. I was wondering if anyone else has faced this issue before and what paper or resources they would recommend. Thanks for the help submitted by /u/Divine_Invictus [link] [comments]

I’ve been working in data for a while and decided to finally get my masters a year ago. This term I’m taking an advanced visualization course that’s focused on dashboard optimization. It covers a lot of good content in the readings but I’ve been shocked to find that the practical portion of the course revolves around R Shiny! I when I first heard of R Shiny a decade or more ago it was all the rage, it quickly died out. Now I’m only hearing about Tableau, power bi, maybe Looker, etc. So in your opinion is learning Shiny a good use of time or is my University simply out of touch or too cheap to get licenses for the tools people really use? Edit: thanks for the responses, everyone. This has helped me see more clearly where/why Shiny fits into the data spectrum. It has also helped me realize that a lot of my chafing has come from the fact that I’m already familiar with a few visualization tools and would rather be applying the courses theoretical content immediately using those. For most of the other students, adding Shiny to the R and Python the MS has already taught is probably the fastest route to that. Thanks again! submitted by /u/theSherz [link] [comments]

I analyzed 18 recent papers on reasoning model limitations and found something disturbing: these models don't fail gracefully like humans do. They maintain high performance right up to a complexity threshold, then collapse entirely. Key findings: - The cliff is real: Models solving 10-step reasoning chains at 85% accuracy don't gradually degrade. They maintain that 85% until around step 12, then plummet to near-random guessing by step 15. - Composition breaks catastrophically: A model with 90% math accuracy and 85% commonsense accuracy drops to 55% when doing both together. They don't combine capabilities - they fragment them. - Chain-of-thought can hurt: In medical diagnosis tasks, 86.3% of models performed *worse* with CoT prompting. They talk themselves out of correct answers. - Scaling inference compute doesn't help: The Quiet-STaR approach spent $200 per query for 32% accuracy on complex reasoning. Humans: similar accuracy, 30 seconds, free. The production implications: Current benchmarks (MMLU, ARC-AGI) only test within narrow complexity bands. Your 95% test accuracy means nothing if those tests don't probe the cliff edge. I've included a production routing system example that handles this reality - routing by complexity detection with fallback logic for when models hit their limits. Full analysis with charts and code: https://rewire.it/blog/the-complexity-cliff-why-reasoning-models-work-until-they-dont Discussion: Are we fundamentally limited by transformer architecture, or is this solvable with better training methods? submitted by /u/Fair-Rain3366 [link] [comments]

Here’s why: A company opens a position and I apply along with 800 other people. The company sees 800 resumes and says F that, we’re hiring a recruiter. The recruiter finds me on LinkedIn and says they have a great job for me. Of course it’s the one I applied to. They ask if I’ve already applied and I tell them the truth, they ghost me because they don’t get commission if they’re not the original source. A few days after this, another recruiter reached out about a different position that I was planning on applying to directly with the company. This is also something that my current company has done after being overwhelmed with too many applicants. I’ll still be applying to some jobs, but it’s weird that applying has seemed to hurt my chances in some situations. Has anyone else experienced this? Any strategies for handling this? submitted by /u/Fit-Employee-4393 [link] [comments]

What is the current status of university-affiliated researchers getting access to uncensored versions of the largest LLMs today? Public-facing versions of GPT-5, Gemini 2.5, and Grok are both highly censored and tightly tuned by invisible prompts unseen by the user that turn them into helpful assistants for user tasks. Attempts to subvert these gaurdrails is called "jailbreaking" and the public LLMs have also been tuned or reprogrammed to be immune to such practices. But what does the workflow with a raw LLM actually look like? Do any of the larger tech companies allow outside researchers to interact with their raw versions, or do they keep these trillion+ parameter models a closely-guarded trade secret? (edit: After reading some replies, it appears the following must be true. ALl these IQ test results that keep popping on reddit with headlines about "..at the Ph.d level" must all be tests performed in-house by the coporations themselves. None of these results have been reproduced by outside teams. In academic writing this is called a "conflict of interest" and papers will actually divulge this problem near the end right before the bibliography section. These big tech companies are producing results about their own products, and then dressing them up with the ribbons-and-bows of "Research papers" when it is all just corporate advertising. No? Yes?) submitted by /u/moschles [link] [comments]

Hii All. A use case has arised for implementing a Graph Database for fraud detection. I suggested Neo4j but I have been guided towards the Neptune path. I have surface level knowledge on Graphs. Can anyone please help me with a roadmap and resources on how I can learn it and go on with the implementation in Neptune? My main aim is to create a POC as of now. My data is in S3 buckets in csv formats. submitted by /u/NervousVictory1792 [link] [comments]

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Hello all !! I need your help to tackle this particular problem statement I want to solve: Suppose we have to devise an algorithm to classify sources of underwater acoustic signals recorded from a single channel hydrophone. A single recording can have different types/classes of sounds along with background noise and there can be multiple classes present in an overlapping or non overlapping fashion. So basically I need to identify what part of a recording has what class/classes present in there. Examples of different possible classes: Oil tanker, passenger ship, Whale/ sea mammal, background noise etc.. I have a rough idea about what to do, but due to lack of guidance I am not sure I am on the right path. As of now I am experimenting with clustering, feature construction such as spectrograms, mfcc, cqt etc. and then I plan to feed them to some CNN architecture. I am not sure how to handle overlapping classes. Also should I pre-process the audio but how, I might lose information ?? Please just tell me whatever you think can help. If anyone has some experience in tackling these type of problems, can you please help me. Suggest me some ideas. Also, if anyone has some dataset of underwater acoustics, can they please share them, I will follow your rules regarding the dataset. submitted by /u/carv_em_up [link] [comments]

Hey everyone, I’m running a product that uses a large AI provider’s model for some specialized functionality. The system processes around 500k requests per month, which adds up to roughly 1.5B tokens in usage. The product generates customer interaction data that could, in theory, help the model provider improve their systems. They recently reached out saying they’d like to explore a “mutually beneficial collaboration” involving that data, but they haven’t given any concrete details yet. My guess is they might propose something like free usage or credits in exchange. Before I consider anything, I plan to update my Terms of Service and notify users about what’s collected and how it’s used. Still, I’m trying to make sure I don’t end up giving away something valuable for too little - the data could have real long-term value, and usage costs aren’t cheap on my end either. What I’m trying to figure out: • What should I ask them before agreeing to anything • Should I request an NDA first • How do I handle ownership and pricing discussions so it’s actually fair • Any red flags or traps to look out for in deals like this Would really appreciate advice from people who’ve done data or AI-related partnerships before. submitted by /u/Round_Mixture_7541 [link] [comments]

WACV 2026 Final decisions are expected to be released within next 24 hours. Creating a discussion thread to discuss among ourselves, thanks! submitted by /u/akshitsharma1 [link] [comments]