https://research.google/blog/ds-star-a-state-of-the-art-versatile-data-science-agent/ Has anyone tried it? I would like to know your opinion submitted by /u/FinalRide7181 [link] [comments]

Created a slide deck with relevant paper links to illustrate brief history of LLM Post Training https://github.com/samrat3264/llm_post_training_history/blob/main/Post-Training%20Soup.pdf submitted by /u/Internet_Problems [link] [comments]

Imagine your ML development environment running inside a web platform where each tool such as Jupyter, VS Code, or a labeling app runs in its own container and opens directly in the web application. There are no virtual desktops or VDIs, no local setup, and no dependency conflicts. The underlying platform manages GPU scheduling, networking, and storage automatically. Each container would start in seconds on pooled GPU or CPU nodes, connect to centralized file or object storage for notebooks and datasets, and shut down cleanly when idle. Your code, libraries, and outputs would persist between sessions so that when you log back in, your workspace restores exactly where you left off without consuming any idle compute resources. The base infrastructure still includes the familiar layers of hypervisors, GPU drivers, and shared storage that most ML clusters rely on today, but users never need to interact with or maintain them. From a user’s point of view, it would feel like opening a new browser tab rather than provisioning a virtual machine. I am curious how this kind of setup would affect daily ML workflows: Would reproducibility improve if everyone launched from a common base image with standardized dependencies and datasets? Would faster startup times change how you manage costs by shutting down sessions more often? Where might friction appear first, such as in data access policies, custom CUDA stacks, or limited control over environments? Would you still prefer a dedicated VM or notebook instance for flexibility, or would this kind of browser-based environment be enough? How could this approach influence collaboration, environment drift, or scaling across teams? Not affiliated with any platform. Just exploring how a web platform that delivers ML tools as browser-based containers might change the balance between speed, reproducibility, and control. submitted by /u/Majestic_Tear2224 [link] [comments]

Decisions still haven’t been released. CVPR allows dual WACV submissions. How is it different than just a dual submission moment after WACV round 1 reviews were in. This has to be one hell of a serious mishap. submitted by /u/casualcreak [link] [comments]

Hi everyone, just sharing a couple of slides about Gemma3n architecture. I found it a very interesting architecture with a lot of innovations (e.g. Matryoshka Transformers, MobileNetV5, PLE, etc) that are very rare to see nowadays. Given that there weren't much information about the model, I decided to dig further and made a couple of slides for those interested. submitted by /u/perone [link] [comments]

Hey all, I am excited to share our new pre-print with you. GRAM: a General-purpose Real-world Audio Model to efficiently learn spatial audio representations. We tried to adress two main limitation of recent foundation models. (1) The performance drop of recent audio foundations models on real-world acoustic environments with reverberation and noise. (2) The inherent spatial nature of real-world sound scenes is overlooked and tasks involving sound localization ruled out. Therefore, we proposed GRAM-Binaural (A Binaural foundation model that can perform extremely well on general purpose audio representation learning, and do localization), and GRAM-Ambisonics (Similar to binaural, but has better localization properties). https://preview.redd.it/cqmwxkxobuzf1.png?width=1085&format=png&auto=webp&s=7bd8785f3efddd813115d22c56721de76e53f7c4 The results were very interesting. GRAMs showcased that naturalistic training (training with reverb + noise) is actually beneficial for both dry (HEAR) and naturalistic scene (Nat-HEAR) (audio with reverb + noise + spatial) performance. And, GRAMs surprassed state-of-the-art spectrogram foundation models with fraction of the data. Furthermore, GRAMs could localize sounds without specialized localization pre-training unlike other models. This marks GRAMs as the first audio foundation model that is available in both a two-channel, binaural format and a four-channel, first-order ambisonics format. To see more experiments, and read more in depth please see: Paper: https://arxiv.org/abs/2506.00934 Code: https://github.com/labhamlet/GRAM-T To try GRAMs, please use the huggingface endpoints: https://huggingface.co/labhamlet Looking forward to a nice discussion! submitted by /u/ComprehensiveTop3297 [link] [comments]

https://preview.redd.it/7u5do1x19uzf1.png?width=1103&format=png&auto=webp&s=bfc314716f4e33593b16e6e131870dae62d7577a Hey All, We have just released our new pre-print on WavJEPA. WavJEPA is an audio foundation model that operates on raw waveforms (time-domain). Our results showcase that WavJEPA excel at general audio representation tasks with a fraction of compute and training data. In short, WavJEPA leverages JEPA like semantic token prediction tasks in the latent space. This make WavJEPA stand out from other models such as Wav2Vec2.0, HuBERT, and WavLM that utilize speech level token prediction tasks. In our results, we saw that WavJEPA was extremely data efficent. It exceeded the downstream performances of other models with magnitudes of less compute required. https://preview.redd.it/7uxj7wgz9uzf1.png?width=1084&format=png&auto=webp&s=6d05cf829a65bfaec5871dfe0487e4d11c80b132 We were further very interested in models with good robustness to noise and reverberations. Therefore, we benchmarked state-of-the-art time domain audio models using Nat-HEAR (Naturalistic HEAR Benchmark with added reverb + noise). The differences between HEAR and Nat-HEAR indicated that WavJEPA was very robust compared to the other models. Possibly thanks to semantically rich tokens. Furthermore, in this paper we proposed WavJEPA-Nat. WavJEPA-Nat is trained with naturalistic scenes (reverb + noise + spatial), and is optimized for learning robust representations. We showed that WavJEPA-Nat is more robust than WavJEPA on naturalistic scenes, and performs better on dry scenes. As we are an academic institution, we did not have huge amounts of compute available. We tried to make the best out of it, and with clever tricks we managed to create a training methadology that is extremely fast and efficent. To go more in-depth please refer to our paper and the code: Paper: https://arxiv.org/abs/2509.23238 Code: https://github.com/labhamlet/wavjepa And, to use WavJEPA models, please use our huggingface endpoint. https://huggingface.co/labhamlet/wavjepa-base Looking forward to your thoughts on the paper! submitted by /u/ComprehensiveTop3297 [link] [comments]

I see "Modified 5 November" on the latest updates on Openreview. This probably implies that AAAI-2026 results are imminent within a day or so. I'm opening up this thread for you to post your scores (and their associated confidences) and results, but please also mention what category (CV etc.) you submitted to, and whether or not you provided additional experimental results in your 2500-character rebuttal (even if the instructions said not to - I've noticed many authors in my review stack have done this anyway). Other points of discussion are also welcomed! submitted by /u/Adventurous-Cut-7077 [link] [comments]

Is OpenReview down for anyone else? Great timing — right ahead of the CVPR registration deadline. Here’s the funny (and painful) part: I submitted my paper earlier with only myself as the author, planning to add my co-authors and PI later once our final results were ready. And now… the site’s down, and I can’t access anything. P.S. The deadline is in just about 4 and a half hours. submitted by /u/Outrageous_Tip_8109 [link] [comments]

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]