Hey folks I'm starting to hear that ROCm works fine for inference now. But, I've not seen any reports on how viable it is for training. I have a couple of RTX 3090s I use for prototyping models, but I'm considering switching to a pair of RX7900XTX instead. On paper at least, the RX7900XTX can output about 4 times the throughput at FP16 with a similar power draw, VRAM, and cost. Based on PyTorch docs, it seems like ROCm is now fully supported, but I'm struggling to find user reports on how well PyTorch runs with ROCm instead of CUDA. How viable is it to switch over to ROCm at the moment? Is it at the "it just works" stage yet? Or is the AMD ecosystem still significantly behind CUDA? submitted by /u/QuantumQuokka [link] [comments]

This is an interactive math reference for transformer models, presented via dataflow graphs, all the way down to elementary math. Covers models from GPT-2 to Qwen 3.6, with MLA, MoE, RoPE, MTP, hybrid attention, and other variants toggleable. Originally made this for myself to keep track of all the variations. If you find errors or find something unintuitive or misleading let me know! submitted by /u/simonramstedt [link] [comments]

Hi! I am trying to sanity-check an assumption for diffusion video generation reproducibility. Suppose I run the same video diffusion model on two different GPU architectures, with: identical model weights and implementation (same attention backend, etc) identical prompt and parameters (same number of denoising steps, etc) deterministic sampler (no extra noise is injected during inference) the exact same starting noise latent Could I expect more or less the same generated video? I understand that there's no way to guarantee bitwise-identical outputs due to floating-point math differences, but could it realistically make the generated videos so different that it'd be immediately noticeable to a human eye? Or would one normally expect only tiny pixel-level/minor perceptual differences? submitted by /u/hellosandrik [link] [comments]

Thread to consolidate discussion/sharing for early accept/rebuttal/rejection for MICCAI 2026! submitted by /u/kw_96 [link] [comments]

submitted by /u/Benlus [link] [comments]

I have a dataset of around 150k stool images; growing at 300+ images per day, and I’m trying to better understand the “right” way to use it for training a computer vision model. Right now, our process is pretty manual. We initially trained on about 5k images that were individually verified by a human. For every image, we checked/corrected the Bristol type, consistency, color, mucus/blood indicators, etc. Then we trained the model on those verified annotations. As we continue training, we keep doing the same thing: manually reviewing and correcting images before feeding them back into the model. My question is basically: does this workflow make sense from an ML perspective? Is this how people normally approach building a solid vision dataset/model, especially in a domain where annotation quality matters a lot? Or is there a smarter/more scalable approach people usually move toward once they have a large dataset? I’m mainly trying to understand best practices around dataset quality, human verification, iterative training, and scaling annotation without introducing bad labels. submitted by /u/SamePersonality5183 [link] [comments]

I'm genuinely curious, because I've been seeing some papers come out recently from the ILP world, like referenced above as well as others [1, 2]. It seems they're busy cooking. In the main linked paper they're tackling pure image datasets and predicate induction which I've previously read was very difficult for ILP. They're claiming strong performance. Could ILP ever viably compete in DL/NN dominated spaces like machine vision, stable? submitted by /u/Pzzlrr [link] [comments]

Neurips deadline crunch stress post. template has no new page after references before appendices this year but all camera ready papers from last year have this. looks hella awkward to have appendices start on same page as references. is adding a /newpage ok/required/not ok/etc? TIA submitted by /u/baghalipolo [link] [comments]

Hey everyone! I’d like to share a personal project that’s still in its early stages, focused on black‑box optimization algorithms. I’m open to feedback, suggestions, or any questions you might have. You can check the full overview here: https://github.com/misa-hdez/sgo-lab/blob/main/docs/project_overview_en.pdf Feel free to explore the repo for more details: https://github.com/misa-hdez/sgo-lab I’d love to hear your thoughts! submitted by /u/Mis4318 [link] [comments]

**Update: my questions have been escalated to their teams. I'll share their answers (& hopefully reassurance) here.** Weights & Biases sent an email yesterday, saying their new Master Service Agreement takes effect May 11th. I use & love wandb, but I'm concerned about the changes. I wanted to start a discussion. I sent them an email, but I think I'm too small to hear back. How do you interpret these changes? Do you worry about intellectual property rights? Do you need an enterprise contract for true protection? Weights & Biases defines Customer Data as "any data, content or material that Customer (including its Authorized Users) inputs into the Software or Service, *including machine learning models and deep learning research projects, and any visualizations, analyses, and other reports generated by the Software or Service.*" Who Owns Your Research? In the prior agreement, Section 8(b) made this clear: > As between the parties, *Customer owns and retains all right, title and interest in and to the Customer Data.* Except for the rights granted to W&B in Section 4(a), Customer does not by means of this Agreement or otherwise transfer any other rights to W&B. The new agreement deletes these statements entirely. Customer Data is added to Section 6(e), meaning it survives after terminating a subscription. How can Weights & Biases use your data? In the prior agreement: "Customer may transfer Customer Data to W&B and W&B may use Customer Data *to provide the Software and Service*. Customer grants W&B a limited right during each Subscription Term to use Customer Data in accordance with this Agreement, the DPA and BAA (as applicable). In the new agreement: "Customer may transfer Customer Data to W&B and Customer grants W&B the right to use Customer Data to (i) provide and improve the W&B Assets, *(ii) develop new product offerings*, and *(iii) for the purposes of providing and improving AI Features*. Customer grants W&B a limited right to use Customer Data in accordance with this Agreement, the DPA and BAA (as applicable). There's now an explicit callout for using Customer Data (models, logs, reports, etc.) to train AI, and there's no acknowledgement of an opt-out system. The agreement does say "W&B may use Customer Data from free and academic customers for testing and development purposes." But then it fails to differentiate treatment for Pro and Enterprise customer data. The prior agreement is available on Wayback Machine here: https://web.archive.org/web/20260227104844/https://wandb.ai/site/terms/ submitted by /u/algorithm477 [link] [comments]

In the TGS Salt Identification Challenge hosted by Kaggle, the model automatically developed by the AIBuildAI Agent ranked in the top 5.7% out of 3,219 human teams composed of human experts. Model and code developed by the Agent: tasks/tgs-salt-identification-challenge. https://preview.redd.it/o9h3pkf9ojzg1.jpg?width=1800&format=pjpg&auto=webp&s=b648eb38f89a1e48af5d0bb36245dcc9bf3ead01 submitted by /u/pengtaoxie [link] [comments]

submitted by /u/CryoSchema [link] [comments]

It’s shocking how frequently I notice hallucinated citations. For citations of my own papers, I’ve seen 5 in the past couple of months, where the the title is correct but the author list is wrong. When I email the author to let them know, they always blame an LLM for hallucinating. Is it really that hard to populate the .bib yourself? If you have any respect for research, is it not a basic requirement to make sure you correctly cite the prior literature? I feel there should be harsher penalties for these hallucinated citations. Are others experiencing the same? submitted by /u/Pure-Ad9079 [link] [comments]

I wonder how this AC-Pilot thing works for NeurIPS 2026. The guidelines say that "What you are communicating is that the authors do not need to worry about concerns you have not listed, and that there is a real opportunity for acceptance if listed concerns are sufficiently addressed." However if a reviewer sees that their questions are not on that list compiled by the AC, even if all the listed questions are properly addressed that particular reviewer will be less inclined to change the score, no? Also despite that they kept emphasizing it's whether the concerns were sufficiently addressed that matters instead of the raw scores, we all know the raw scores matter, so eventually one still must answer all questions? submitted by /u/dontknowwhattoplay [link] [comments]

Hello everyone. I’m excited to share our new paper! Figure 1: Comparison Across Architectures A lot of recent Transformer variants try to improve information flow across depth by exposing later layers to earlier representations. You may have recently heard about methods like DenseFormer, MUDDFormer, and HyperConnections, which add more dense or dynamic cross-layer pathways. These are expressive, but they can also come with meaningful throughput and memory costs. Our question was more specific: Can we improve the efficiency-performance tradeoff at scale by enabling more principled reuse of early representations? We introduce SATFormer, which keeps the same cheap first-layer value pathway used by value residual learning, but replaces static layer-wise mixing with a per-token, per-head, context-dependent gate. Instead of uniformly copying early features into every later layer, SATFormer learns when and where each head should re-access the first-layer value stream. Main results: Across 130M–1.3B models, SATFormer improves validation loss over both Transformer and ResFormer baselines. On retrieval-intensive benchmarks, SATFormer gets the best average score among the evaluated architectures, narrowly surpassing MUDDFormer and improving over ResFormer by about 1.5 average points. SATFormer runs close to Transformer/ResFormer, whom are roughly 1.75×–1.82× higher throughput than HyperConnections and MUDDFormer. Mechanistic analysis suggests the gate is not just acting like a dense residual shortcut: access is sparse, depth-dependent, head-specific, and stronger for specific tokens. The core framing is that early-representation reuse may be better treated as a retrieval/control problem rather than a connectivity/maximal routing problem. OverllI am excited to discuss what some better approaches may be to improving the transformer architecture while maintaining a high throughput. Arxiv: https://arxiv.org/pdf/2605.03953 github (still WIP): https://github.com/SkyeGunasekaran/SATFormer submitted by /u/Skye7821 [link] [comments]

Hello ML and RL community We are inviting participants to the League of Robot Runners (LoRR) 2026: https://www.leagueofrobotrunners.org Co-located with AAMAS 2026, LoRR is a research competition on large-scale multi-robot coordination. These are important problems in a number of areas including logistics, manufacturing and computer games! In this competition, hundreds or even thousands of robots work together to complete tasks and move efficiently across diverse maps, continuously, in real-time and at scale. We believe ML and RL methods could be especially useful for these kinds of problems: The best known algorithms for computing next moves are policy-based Agents operate under uncertainty (move actions have a probability of being delayed) The challenge involves nested combinatorial problem solving (task assignment + path planning) -- a very difficult proposition for symbolic/GOFAI techniques! This is an exciting opportunity to put your ML/RL ideas to the test on a large-scale multi-robot challenge You can participate for fame, glory and cash prizes across three distinct tracks: Task Scheduling Track Execution Track Combined Track We provide a start kit (C++/Python), example instances, validators, and a visualiser. Submissions are evaluated automatically with live leaderboard feedback. Timeline: 16th April 2026: Main Round Begin 22nd May 2026: AAMAS prize deadline AAMAS 2026: AAMAS Prize Announcement 22nd July 2026: Main Round End Early August: Winner Announcement All approaches are welcome: search/planning, RL/ML, OR, mathematical programming, robust optimization, and hybrids techniques. Visit our website for more details (www.leagueofrobotrunners.org) or post here if you have questions! submitted by /u/robotrunnersofficial [link] [comments]

Hi all! I am a bioinformatician and I am working on learning some ML tools for some disease/biomarker stuff. I am working with sparse PLS-DA at the moment. Before actually tuning the model, I run on overall global model (without sparsity) to get an idea of what my data looks like and to get to a starting point. Here is what that global model ends up looking like: global model So from this, I'm seeing that I should include 2 latent components in my model tuning and I chose to use the centroids.dist. So I tune the model with two components, it gives me the # of features to keep on each component and then I run the final model. However, when I do performance assessment on the final model, it looks like this: final model (sparse) I guess I am a little confused. From what I am reading online, and from my own data, error rates should go down with added components. It also doesn't make a ton of sense to me because I should have only picked the features that best distinguish two conditions, so again, I should be seeing error rates decrease. Can someone please help me understand what I'm seeing here and what could be causing this? I am still learning how all of this works, so amy sort of guidance is appreciated. Thank you! submitted by /u/dacherrr [link] [comments]

Hey guys, Just saw that NeurIPS this year might be exceeding 40k, what submission number did you get? The max I know of was 29k, that was 24 hours ago submitted by /u/StriderKing27 [link] [comments]

Hi all, I’ve spent the last 3 years working on Radar Perception for a legacy automotive project in Germany. My background is an MSc in Robotics & AI. Currently, I spend my time analyzing point clouds and SNR distributions to debug failures. It’s mathematically complex, but I’m not implementing any models or designing systems. I feel like I'm becoming a "PowerPoint Engineer" who knows a lot about noise but isn't building the future of autonomy. I want to move into Applied ML/Autonomy, but I’m worried my 3 years of "analysis" don't count as "development experience." Does it make sense to build a portfolio of ML/Robotics projects applied to Radars to prove I can actually code, or will recruiters only care about my work? Is this a good path for applied ML or i am kidding my self? submitted by /u/Huge-Leek844 [link] [comments]

I got an interview invitation for a Machine Learning Engineer role at a FAANG company. There are two issues. I am not an MLE, so preparing for it feels nearly impossible. Also, I have never even interviewed for an MLE interview, let alone at FAANG. I am currently a Data Scientist and have been interviewing, so I feel good about my preparation for DS roles. Can I tell the recruiter that I believe I am a better fit for a DS role than MLE? Do you have any other suggestions? submitted by /u/Lamp_Shade_Head [link] [comments]