标签：#MachineLearning

Expecting a rejection from NeurIPS given our scores of 5/2/2. Trying to decide between ARR vs. TMLR, but thinking NAACL findings are more likely than main conference. Would you rather have TMLR or *ACL findings on your publication list? Genuinely curious to hear what people have to say. submitted by /u/Pure-Ad9079 [link] [留言]

Sleep is the ultimate black box. We spend a third of our lives doing it, yet we have almost zero data on what happens during those eight hours—unless you're willing to pay for an expensive sleep clinic. Today, we’re going to change that by building a high-fidelity Sleep Apnea and Snore Monitoring system using Whisper-v3 , Librosa , and PyAudio . In this tutorial, we will tackle Whisper-v3 audio processing , real-time sleep apnea detection , and audio fingerprinting to filter out the sound of your fan or your neighbor's car. If you've been looking for a "Learning in Public" project that combines deep health-tech with high-performance Python, you’re in the right place. 🚀 The Problem: Noise vs. Signal Detecting sleep apnea isn't just about recording sound; it's about identifying the absence of sound followed by a gasp (the "apnea event"). Standard noise-canceling algorithms often wipe out the very frequencies we need. We need a system that can distinguish between ambient white noise, rhythmic snoring, and dangerous respiratory pauses. System Architecture 🛠️ Here is how the data flows from your bedside microphone to a processed health report: graph TD A[PyAudio Stream] -->|Chunked Audio| B(Librosa Pre-processing) B -->|Noise Floor Calculation| C{Is it Snore/Breath?} C -->|Yes| D[Audio Fingerprinting / MFCC] C -->|No| A D --> E[Whisper-v3 Inference] E -->|Timestamped Events| F[Apnea Detection Logic] F --> G[Health Report / Alert] G --> H[Dockerized Storage/API] Prerequisites Before we dive in, ensure you have the following tech stack ready: Whisper-v3 : For high-accuracy audio event tagging. Librosa : For feature extraction and spectral analysis. PyAudio : For low-latency streaming. Docker : To package our environment (handling those pesky C++ dependencies for audio). Step 1: Real-time Audio Capture & Preprocessing 🎙️ We start by capturing audio in chunks. We don't want to process 8 hours of silence, so we use Librosa to calculate the Root Mean Square (RMS) energy. impor

Hey Guys, I built a small open-source tool that checks whether a RAG application retrieves documents a user shouldn’t have access to. It supports offline test cases and live HTTP API testing with bearer token/API-key auth. I’m looking for a few engineers to try it on a test or non-sensitive environment and tell me whether it catches anything useful or what would make it better. GitHub: https://github.com/InfraGuard-Labs/rag-access-check submitted by /u/Lostboy_journey [link] [留言]

Hello all, here is a chill post. When I was an undergrad, I really liked working things out on a whiteboard. Drawing stuff, talking through ideas out loud, testing little hypotheses. Now I work in radar DSP, and a lot of my work is code, numerical experiments, deep learning and waiting for training to finish 😅 I’m wondering how other people bring that whiteboard style of thinking into DSP, data science or ML work. Do you still use a whiteboard regularly, or do you mostly go straight from idea to code? submitted by /u/Huge-Leek844 [link] [留言]

You can beat SOTA Time Series Anomaly Detection methods with a 100 year old algorithm Time Series Anomaly Detection (TSAD) seems to be one of the hottest topics in NeurIPS, SIGKDD, VLDB etc. Many (perhaps most) papers evaluate on Paparrizos’ TSB-AD-M benchmark… However, I tested these benchmark datasets and found that in most cases I could beat the SOTA TSAD methods with a 100-year-old algorithm, simple Statistical Process Control (SPC). In the attached example, SPC gets perfect results. If we can beat the SOTA papers with 100-year-old algorithm, we probably should not be too impressed with them [b]. I really think this calls for some introspection by the community. To be clear, I make no claims (here) about the proposed algorithms in all these paper. But the TSB-AD benchmark is obviously too trivial to make meaningful claims on [a][b]. The example shown is one of the ECG traces but look at dozen of traces marked “TAO”, they are even more trivial to solve with SPC [a][c]. I do not claim to have solved the triviality problem, but I have done 90% of the work to introduce more challenging TSAD problems ([d] sled dogs, [e] Tuna, Fuel Cells, Smart Manufacturing etc.). TLDR: I think the TSAD community needs more introspection on benchmarks. Most progress over the last decade seems to be illusionary. [a] https://www.youtube.com/watch?v=VftCMSI3C_s [b] https://www.dropbox.com/scl/fi/31zuyhejb6sdjrom20frn/Problems-with-Time-Series-Anomaly-Detection.pptx?rlkey=mvcj1wz5s45kgazezopnih2h7&dl=0 [c] https://www.dropbox.com/scl/fi/42fkf9q9hft2224dnm83v/The-TSB-AD-Benchmarks-are-Nonsense.pptx?rlkey=5fwjopie5ncjhkgr0wqhdm2lp&dl=0 [d] https://www.linkedin.com/feed/update/urn:li:activity:7488825356494237696/ [e] https://www.dropbox.com/scl/fi/hettphvtpyrksggfect9d/Tutorial-on-Pan-Matrix-Profile.pptx?rlkey=p59gd2w56fxl9kl2fh5q819oo&dl=0 submitted by /u/eamonnkeogh [link] [留言]

https://preview.redd.it/42s57e5oqamh1.png?width=1903&format=png&auto=webp&s=69958a72e22276534b3605d11f3e1721f76e59c9 Disclosure: I developed AIStupidLevel, the open-source system used to collect and analyze this data. Both the frontend and backend are MIT-licensed. Most LLM evaluations measure performance at a single point in time. I wanted to investigate a different question: How stable are the models behind production APIs over time, and how can sustained performance changes be separated from ordinary stochastic variation? I built a continuous evaluation pipeline that repeatedly tests models across coding, deep reasoning, tool calling and high-frequency canary tasks. Dataset and evaluation process For this analysis, I examined: 31,352 hourly benchmark scores 49 model identifiers Multiple providers and model families Repeated measurements using consistent tasks and scoring A normalized 0-100 composite score Coding responses are executed rather than judged only through model-based evaluation. Tool-calling tests require models to select tools, construct valid arguments and complete workflows inside isolated Docker environments. Tasks are executed five times, with the results aggregated to reduce the influence of unusually strong or weak generations. Prompts, scoring logic and API parameters are kept consistent wherever supported by the provider. Main result The observed score variation was: Within the same day: 2.8 points Between different days: 8.4 points Between-day variation was therefore approximately 3× greater than within-day variation. This suggests that isolated hourly movements are dominated by normal model stochasticity, while sustained changes across daily evaluation windows provide a materially stronger signal for detecting performance drift. The detection pipeline aggregates repeated measurements into daily medians and applies sequential change-point detection. Potential incidents must persist beyond the expected historical variance and pass statistical

How much of a disadvantage is it if your only internship is not at one of the big frontier labs when it comes to post-phd opportunities in robotics/ML? My PhD is at a top university (UK) and my internship is interesting and relevant but the team itself is smaller and it's no Nvidia/Google/etc. Most places here prefer interns for 6 months so I'm not sure if I can do another internship down the line or if there's even much point compared to just wrapping up the PhD and then getting a job. Is having a relevant internship already a big plus when it comes to applying to industry, or does it need to be a really well known big tech company? submitted by /u/IgneousPutorius [link] [留言]

Hey everyone, I'm an international student studying in the US. I'm on track to graduate late next year. My research is not exactly ML, it is in 3D computer vision but have decent exposure to ML as well. In case you didn't know, the CPT program (which let's internation students do internships) has been suspended by many top universities (UC Berkeley, UIUC, Purdue, UNC, UCLA, stanford, etc). Given that there is now no way for me to do an internship, how hard will it be for me to get a job when I'm nearing graduation? I have 3 papers in CVPR, 3DV and ICRA (robotics conference) and hope to publish 2 more at next year's ICCV and neurips before graduating. I'm just worried that all my hardwork will go for a waste because of this policy change (I'm from a 3rd world country, so not much opportunity back home). To be crystal clear, I'm not asking for legal advice, just wanted to know in your experiance, have you seen anyone (international student) get into good industry labs without internships? EDIT: thanks so much for everyone for the quick replies! If it helps, my specific research area is 3D reconstruction, and I've been focused on Gaussian Splatting recently, if this info helps anyone help me! submitted by /u/Fit-Raccoon4534 [link] [留言]

This article is about running a hand-written Gemma 4 port in pure JAX on three different accelerators, and about the two places the abstraction leaks. The code is here: github.com/xbill9/gemma4-dev What is this project trying to Do? This project aims to serve one Gemma 4 checkpoint from one JAX port across every accelerator I can rent, and to find out — by measurement, not by reading docs — which parts of "it's just JAX" are true. The port lives in ports/gemma4/ and is driven by a generation loop behind an OpenAI-compatible server. No PyTorch, no vLLM, no torch_xla . The same code runs on Cloud TPU v5e and v6e, and on an NVIDIA T4G attached to an AWS Graviton2 host. "Pure JAX" is the whole experiment. If the port is really portable, the only thing that should change between those rigs is a config file. It mostly is. Two things are not, and they are the interesting part. Gemma 4 E2B is not a stock transformer Any port has to carry four irregularities, and none of them are optional: Two attention geometries. Sliding layers use head_dim=256 , global layers use 512 . Most inference stacks assume one head dimension per model. 8:1 MQA , so the KV budget is nothing like the parameter count would suggest. A KV-share map that collapses 35 layers onto 15 caches . A 512-slot sliding ring , plus per-layer embeddings (PLE) held in a 4.70 GB table that gets quantized to 4 bits on load. That first one is worth dwelling on, because it is what breaks other stacks. On the vLLM path, the heterogeneous head dims force the Triton attention backend: Gemma4 model has heterogeneous head dimensions {'sliding_attention': 256, 'full_attention': 512}. FA4 not available, forcing TRITON_ATTN backend. And on a Turing GPU that backend then asks for shared memory the hardware does not have: triton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536 JAX never enters that conversation. Attention is ordinary XLA rather than a hand-tiled kernel, so ther

This article provides a step by step deployment guide for serving Google's Gemma 4 on an AWS EC2 G5g instance using pure JAX. The code is here: github.com/xbill9/gemma4-dev What is this project trying to Do? This project aims to serve a modern open model on the cheapest whole CUDA GPU AWS will rent you, and to measure honestly what that costs. Aren't You Using The Wrong GPU? Probably! The T4G is a Turing chip from 2018. It has no bfloat16 and no fp8. But it is cheap, it is available when nothing else is, and it is attached to a Graviton2 host — which makes G5g the rare hardware axis that almost nothing in the ML ecosystem targets: aarch64 and CUDA together . So let's give pure JAX a shot on G5g! AWS EC2 G5g G5g instances pair an AWS Graviton2 (64-bit Arm) processor with NVIDIA T4G Tensor Core GPUs. At g5g.xlarge they are the cheapest EC2 instance carrying a whole NVIDIA GPU , and the only Arm-based GPU family AWS offers. Two GPU instances are cheaper per hour and neither can serve this model (us-east-1, Linux, on-demand, checked against the Pricing API on 2026-08-28): g6f.large at $0.2020 is genuinely NVIDIA and genuinely CUDA — but it is one eighth of a GPU with 3 GB , and the weights alone are 6.155 GB. The first g6f that fits is g6f.4xlarge at $0.9500, which is 1.7x this rig's g5g.2xlarge . g4ad.xlarge at $0.3785 carries an AMD Radeon Pro V520 — no CUDA at any price. Among whole NVIDIA GPUs, G5g is the floor: g5g.xlarge at $0.4200, and the next one up is g4dn.xlarge at $0.5260. More information is available here: https://aws.amazon.com/ec2/instance-types/g5g/ The default in this rig is g5g.2xlarge — 1 GPU, 8 vCPU, 16 GiB RAM. Note- the T4G reports 15,360 MiB of device memory, not the nominal 16 GB. Budget against the measured number. Gemma 4 Gemma is Google's family of open models built from the same research as Gemini. This rig serves google/gemma-4-E2B-it , the instruction-tuned reference release. JAX JAX is Google's array computing library — NumPy semantics, c

I'm trying to understand what a world model is. I understand it has its roots in cognitive science and reinforcement learning. I understand at least at the moment what most people are building which they call world models are fancy video generation models. But what actually counts. Does a simulator count as a world model. Some "world models" are described as simulators, or rather a simulator is described as one type of world model. But is a simulator like lets say a physics engine a world model? There are some video game world models or computer use world models. Would a hardware/video game emulator count as a world model? And can a digital twin also be a world model with some additional features. I've seen a definition that says a world model should "operate on learned representations, not exclusively hand-crafted physics i.e. a physical referent is optional." Which is fair enough but then would a physics accelerator that uses a ml count as a world model? Like some ML fluid simulator is that a fluid world model? Are world models just a rebrand of simulation or is there really a fundamental difference? Should the definition be limited to models that aim to generally model all of the real world? So that would exclude video game world models and also models of specific interactions. submitted by /u/neutrino_boy [link] [留言]

Its a 2.4-4 million parameter model, quantized to int8, that can be fully executed on the microcontroller in ~20s with the longest generation. The generated image will then be displayed on a monitor or transferred via usb. Its a latent flow transformer with 12 layers using AdaLN-Zero for conditioning. CFG is also supported and boosted the image quality a lot. The inference engine streams the weight via DMA from the flash while the previous layer is computed. Relu² activation was used to increase sparsity, which the engine can use to skip calculations. Took a lot of ablations to get it right and I am quite astonished I got so far with so few parameters. Will post the repo below https://preview.redd.it/psu567et66mh1.png?width=1167&format=png&auto=webp&s=8b69dfe5caf5a0cde79f03f23fb5843c940bf993 submitted by /u/cpldcpu [link] [留言]

Has anyone got the decision notification yet? Please mention decision (e.g., approved/rejected) and geographical area (e.g., North America) in your answer. I know the official notification date is 31 August, but putting this here before hand so folks can post updates asap when they get them. submitted by /u/RevolutionaryIssue59 [link] [留言]

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. submitted by /u/didimoney [link] [留言]

Classic Machine Learning Through the Eyes of an SRE — Part 9: Isolation Forest The algorithm in one line: Isolation Forest scores how anomalous a point is by how few random cuts it takes to separate that point from everything else. No model of normal, no loss function, nothing optimized. ← Previous: Part 8 — Hierarchical Clustering Fails Beautifully · Next: this is the series finale — start at Part 1 . Every anomaly detector I had studied models what NORMAL looks like, then calls the leftovers outliers. K-Means: far from every centroid. DBSCAN: in the noise bucket. Sensible, and intuitive. Isolation Forest does not bother. It never models normal at all. It goes straight at the rare points with a single question: how few random cuts does it take to isolate you? Random cuts, literally. Pick a feature at random, pick a split value at random between that feature's min and max, repeat. A point that separates from the crowd in three cuts is anomalous. A point buried in the middle of a dense mass takes thirty. Grow hundreds of these random trees, average the isolation depth for each point, and you get an anomaly score. There is no loss function here. No optimization, not even the local kind that decision trees do at every split. Every cut is a coin flip, and the power comes entirely from averaging, which is the forest trick from the supervised half of this series now applied to pure randomness. Cheap randomness plus averaging beats careful modeling, as long as the target is something randomness naturally exposes. Rarity is exactly that. Sometimes the winning move is to optimize less. That sentence would have gotten me laughed out of my first ML study session. It is also this finale's thesis. The part I had completely backwards Here is the thing I did not know until I read the original paper properly, and it is the opposite of every instinct a decade of ops gave me. Isolation Forest deliberately trains each tree on a small subsample of your data, and this is not a performan

Hey everyone! I’m excited to announce the release of py-evoFE (v0.3.0) — an open-source Python library that uses genetic algorithms to automatically discover, combine, and optimize feature transformations for tabular datasets. GitHub: https://github.com/tanopereira/py-evoFE PyPI: pip install py-evoFE License: MIT The Problem It Solves Feature engineering is still where most tabular ML competitions and production models are won or lost. While GBDTs like LightGBM and XGBoost excel on raw tabular data, they struggle to discover complex ratios, nested group-by aggregations, nonlinear dimensional projections, and interaction graphs on their own. Manual feature engineering is either tedious or constrained by human intuition, while brute-force feature generation explodes the feature space exponentially with colinear noise and high memory usage. What py-evoFE Does py-evoFE searches the space of possible feature recipes using genetic programming: 1. Hierarchical Chaining: Evolved features become building blocks for future generations (e.g., log(ratio(groupby_mean(x1, by=x2), x3)) ). 2. 40+ Built-in Transformers: - Non-linear arithmetic & log-ratios - Target encoding (multiclass, pooled, WoE, quantile target encodings) - String similarity (MinHash, Gap encodings) - Manifold & Dimensionality Reduction (PCA, UMAP, MCA, FAMD, Between-Group PCA) - Graph & Density Clustering (Genie, Lumbermark, MST anomaly scoring) 3. Performance & Speed: - Vectorized computation powered by Polars and PyArrow . - Matrix Hashing & Nearest-Neighbor Caching: Stateful projections (like UMAP and $K$-NN lookups) are cached via byte-hashing to eliminate redundant computation across CV folds. - Multi-Fidelity Screening: Fast low-fidelity CV screens initial populations; only promising candidates proceed to full-fidelity evaluation. 4. Island Model & Caruana Ensembling: - Multi-population parallel search across Ring, Torus, Grid, Hypercube, and Tiered topologies with Gibbs migration. - Post-search greedy Ca

Which research papers (old or new) do you think a PhD student/early researcher must read to improve their writing skills? Do you have a personal favorite researcher whose papers tend to be well-written, in your opinion? Let's define a "well-written paper" as one that clearly explains the problem it is trying to solve, how the method is developed, and the details of the method, while keeping it easy to understand for a general reader (with a basic knowledge of ML, obviously). Also, post-2015-ish papers usually have nice figures to explain their problem/method, and so they tend to be easier to understand. But I am looking for "well-written papers" in terms of the text. PS: I know the best way to learn writing is by actually writing manuscripts, but I am looking for additional reading resources. submitted by /u/fakeaccountlegitme [link] [留言]

Can an AI make other AIs better? And what stops it from just cheating? Last month, an OpenAI eval agent escaped its sandbox and broke into Hugging Face, apparently to grab test solutions from a benchmark. It's exactly what you'd expect from a system that rewrites agents and reads its own grades. We set out to measure recursive self-improvement anyway, with the exam locked outside its sandbox. We introduce HarnessOpt-Bench, which scores an LLM on how much it improves another agent's harness. On the development split, the optimizer sees per-case traces. Upon validation, it receives a single aggregate score. On test, nothing — until a trusted server scores its final candidate harness. API keys, budget enforcement, and held-out data never enter the optimizer's sandbox. That isolation holds by construction, not by instruction: the held-out evaluator and permission control sit outside the loop that evolves the harness. 5 frontier models, 4 downstream tasks, 111 runs to test 2 hypotheses: 1️⃣ Same coding harness, swap the model: Claude Opus 5 under OpenCode tops 3 of 4 tasks. Walk the releases from Nov 2025 to Jul 2026 on one task, and GPT climbs from 3% to 49% of the headroom, Claude Opus from 37% to 59%. 2️⃣ Same model, swap the coding harness: does a model do best in its own? No consistent home-field edge: opencode beats native harnesses (Claude Code, Codex, Kimi CLI) in 11 of 20 model–task pairs. Model choice moves gains 1.8× more than harness choice. Paper: https://arxiv.org/abs/2608.06301 Code (MIT, built on our team's ICML 2026 VeRO): https://github.com/scaleapi/vero Original post: https://www.linkedin.com/posts/shehabyasser_can-an-ai-make-other-ais-better-and-what-share-7498801902260981760-xuCo/ submitted by /u/shehio [link] [留言]

I put together a small model to estimate NeurIPS acceptance based on scores and an assumed acceptance rate. Try it out here: https://levilingsch.github.io/neurips-acceptance-estimator/ submitted by /u/levydawg [link] [留言]

Hey guys, sorry if this is not the appropriate forum for this question. Is anyone going To ECCV and staying in Lund? Apparently a few days back i saw discounted travel pass available for both Malmo and Lund zone but now today I was going to buy it and the registration site says only Malmo pass. Did ECCV remove them? Because deadline to buy them is 28th august. I dont know why they removed it but the organisation this year feels like a mess. Can anyone access it on their registration site if Malmo Lund passes are available? submitted by /u/Marion-De [link] [留言]