标签：#machinelearning

Paper: https://arxiv.org/abs/2605.08172 Workshops: AI for Science & Structured Data for Health at ICML 2026 Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40 o tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight (<2M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures. Hi everyone I’m excited to share my solo paper "Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation" which has been accepted for poster presentations at the ICML 2026 workshops on AI for Science and Structured Data for Health . The project stemmed from my parallel research on structural encoders for biomolecules where enforcing roto-translational equivariance is standard. In this work, I wanted to extend those principles directly to various 3D medical meshes. While current anatomical mesh segmentation methods are highly disjoint and anatomy-specific, we pre

I’ve been reading GPU architecture docs in my free time. NVIDIA PTX, AMD ISA reference guides, Intel Xe, reverse-engineered Apple GPU stuff. Over 5,000 pages across 16 microarchitectures. After a while you notice all four vendors are doing the same 11 things with different names. So I wrote a spec that covers all of them and built a toolchain around it. It’s called WAVE. You write a kernel once, it compiles to a portable binary, then thin backends translate it to Metal, PTX, HIP, or SYCL. Same binary verified on Apple M4 Pro, NVIDIA T4, and AMD MI300X. My co-author Onyinye built PyTorch integration and got identical training results across all backends. Please star on GitHub: https://github.com/Oabraham1/wave Preprint: https://arxiv.org/abs/2603.28793 Read full docs and how I built everything: https://wave.ojima.me pip install wave-gpu submitted by /u/not-your-typical-cs [link] [留言]

Looking for communities where people actually dig into ML/AI research, not hype, not "look what I built with an LLM API," but discussions about papers, training dynamics, debugging real models, infra problems, that kind of thing. I'm specifically interested in places where you can post something like "I'm seeing X behaviour in my SSL training, here's the loss curve, anyone seen this before?" and get thoughtful replies instead of generic advice. submitted by /u/Possible-Active-1903 [link] [留言]

Is this normal? I searched it up and last year it was only 8000. submitted by /u/NightCR_ [link] [留言]

Hey i built Aiki a lightweight tool that let's you chat with Wikipedia locally. https://i.redd.it/67mzfsrc6f3h1.gif what it does: Downloads and chunks wikipedia articles (u can choose those articles by their name or articles and also the option of downloading the similar topics) Uses a custom TF-IDF + cosine similarity retriever (built from scratch) Supports query expansion using Wikipedia links/redirects Optional answer generation with llm Very minimal dependencies and runs completely locally. Repo: https://github.com/yacine204/Aiki Would really appreciate your feedback. submitted by /u/Just_Jaguar3701 [link] [留言]

Nathan Witkin, a research writer at NYU Stern’s Tech and Society Lab, writes damningly about the famous METR AI time horizons graph in the Substack publication Transformer: It is impossible to draw meaningful conclusions from METR’s Long Tasks benchmark — in particular once one realizes that its numerous flaws are probably compounding in unpredictable ways. The appropriate response to a study of this kind is not to assume it can be saved via back-of-the-envelope adjustments, or to comfort oneself that other anecdotal evidence implies that it is probably correct anyway. It is to cut one’s losses and move on in search of higher-quality information. … The METR graph cannot be saved. For all its sleekness and complexity, it contains far too many compounding errors to excuse. Among them is generalizing to the entire species data collected from a small group of the authors’ peers. Coming up with ever more dramatic ways to make this mistake has become a kind of sport among AI researchers. If the field has a central pathology, it is to aggressively overindex on a mix of anecdotal data from power-users, alongside a long list of benchmarks even more compromised than METR’s. One hopes that as the field matures, its participants will learn to stop making these mistakes. The errors include: Some of the human baselines data is not actually measured or collected from any empirical source, rather, it is just guesstimated by the authors A key variable in the data is how long it takes humans to complete certain tasks, but — when METR did actually measure this — it paid its human benchmarkers hourly, meaning they were incentivized with cash to take longer The sample of human benchmarkers was biased toward METR employees’ friends, acquaintances, and former colleagues (who are likely unrepresentative and possibly biased) Humans familiar with a codebase and a specific coding task were 5-18x faster at completing it, but METR used data from humans who were much slower because they had to s

Just thought I'd share, I ran a DCGAN on a dual core RISC-V microcontroller, the CH32H417 generating 64x64 cat faces. This is a new RISC-V MCU, so no TFLite, no CMSIS NN and no external memory. It's a pure C inference engine, bit-identical to PyTorch reference outputs. The model is 12.6M parameters with int8 per channel quantization. Intermediate activations are stored in DTCM and layer weights stream from SD card using double buffering so the next layer loads while the current one computes. The total available SRAM is 512KB shared between both cores and the inference engine and time to generate one image is 26 seconds, it could be faster, but SD card access speed is the bottleneck rather than computation. The z vector is seeded from 200 bytes of quantum random data (ANU QRNG vacuum fluctuation source), transformed via Box-Muller into the latent vector. which is not strictly necessary for image quality but it was a fun constraint for the art installation side of the project. The generated cat is classified as "motivated" or "demotivated" based on a single quantum bit, which selects from a phrase bank with four fragment slots combining into one of 131,072 possible spoken verdicts output through the onboard DAC... As far as I can tell nobody else is running GAN inference on these low cost RISC-V microcontrollers, cause ARM has the CMSIS NN ecosystem for this kind of thing but RISC-V MCUs especially in the CH32 space have nothing, so the entire inference engine is written from scratch. Paper: TinyGAN: Generative Image Synthesis on a RISC-V Microcontroller with Quantum Entropy Sampling submitted by /u/Separate-Choice [link] [留言]

🌟 Announcing the 2nd Workshop on Efficient Reasoning (ER) at @colm2026 — Oct 9! 📣 We welcome submissions! Submit your work here: https://openreview.net/group?id=colmweb.org/COLM/2026/Workshop/Efficient_Reasoning 🗓️ Deadline: July 12, 2026 (AoE) 🔗 Website: https://wdlctc.github.io/efficient-reasoning-2026/ 💬 Topics include (but aren't limited to): 🔹 Multimodal, spatial & embodied reasoning under efficiency constraints 🔹 Curating high-quality reasoning datasets under resource constraints 🔹 Algorithmic innovations for efficient training & RL fine-tuning 🔹 Fast inference: pruning, compression, progressive generation, KV-cache tricks 🔹 Benchmarks & theory on time-/space-complexity and faithfulness 🔹 Systems to deploy long-CoT or on-device reasoning in the wild 🔹 Safety & robustness of efficient reasoning pipelines 🔹 Real-time applications in healthcare, robotics, autonomy, and more 🤝 We invite perspectives from ML, systems, natural & social sciences, and industry practitioners to rethink reasoning under tight compute, memory, latency, and cost budgets. Hope to see you there! 🚀 submitted by /u/Mediocre-Ad5059 [link] [留言]

Hi! I missed securing a main conference ticket for ICML 2026, as my workshop paper got accepted two days ago. Do you believe that it is worth attending just workshops at such A*-tier conferences (with all the overseas travel costs etc.)? I was quite looking forward to attending both, including the talks, poster sessions and company booths. I come from an adjacent field and have therefore had quite a few conference experiences. Any insights into past experience are highly welcome. Thank you! submitted by /u/dreameroutloud [link] [留言]

IAM, DLP, and API gateways are necessary parts of an organization's security stack. None of them...

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