标签：#learning

Hello everyone. The new dataset is named MONET, is Apache 2.0 and available on HF: https://huggingface.co/datasets/jasperai/monet MONET is open, Apache 2.0-licensed image–text dataset. It was built from 2.9 billion images and refined to 104.9 million high-quality samples. We are also publishing a paper that explains how the dataset was created if you are curious and 3 compagnions projects A umap to visualize the distribution A retreival tool to do text or image search A codebase to train T2i model based on MONET Hope this will be usefull! submitted by /u/dh7net [link] [留言]

Why your age-gating algorithm is probably doomed to fail in the wild For developers building in the computer vision and biometrics space, there is a massive gap between a model that passes a NIST benchmark and a system that survives the "child-with-a-VPN" test. Recent data indicates that roughly 32% of children are successfully bypassing age-gating tech. As engineers, our first instinct is often to blame the model—to tweak the weights, gather more training data, or tighten the threshold. But the technical reality is more sobering: the failure isn't in the algorithm; it's in the deployment architecture. The Problem with Probabilistic Logic in Binary Workflows Most age estimation models rely on analyzing biometric markers—skin texture, bone structure ratios, and periocular geometry. They produce a probabilistic age range. However, according to NIST's evaluation of age estimation software, to maintain a low false-positive rate, systems often need to set a "challenge age" between 29 and 33 years. If you are a dev tasked with keeping 17-year-olds off a platform, you are essentially forced to build a "buffer zone" of over a decade. If the system flags anyone who might be under 30, the UX becomes a nightmare. If you lower the threshold to 18, the false-negative rate skyrockets. This is the fundamental trade-off of probabilistic facial analysis: precision and recall are at constant war, and in a high-traffic production environment, the "noise" of real-world variables (poor lighting, low-res sensors, off-axis angles) makes consistency nearly impossible. The Breakdown of the Identity Handoff Beyond the model, there are three technical failure points that no amount of Euclidean distance analysis can fix if the pipeline is broken: The Signal-to-Noise Ratio at Source: Evaluation datasets are clean. Production images are taken on scratched lenses in low-light bedrooms. The delta between training distribution and inference-time reality is where the first 10% of accuracy vanishes.

The AC email says the rebuttal is between 28 to 4th. The June 4th on website is the deadline. So I created this post for the discussion. I know it's a MM conference and less about ML but I think many people here are still submitting there. submitted by /u/Striking-Warning9533 [link] [留言]

AI Glossary: A to Z An A-to-Z glossary of AI terms, created with help from AI itself. Because in 2026, the best way to study AI is apparently to ask AI itself. 🤣 Written for beginners and practitioners alike. Each term includes a plain English definition and a real-world example. Quick Navigation A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z ↑ Back to top A Term Definition Example Agent (AI Agent) An AI system that perceives its environment, makes decisions, and takes autonomous actions to achieve a goal A coding agent that writes, runs, and debugs its own code without human intervention AGI (Artificial General Intelligence) A hypothetical AI that can match or exceed human-level intelligence across any task — does not yet exist Often cited as a long-term goal by companies like OpenAI and DeepMind AI (Artificial Intelligence) The field of computer science focused on building machines that can perform tasks normally requiring human intelligence ChatGPT writing an essay, an algorithm detecting cancer in X-rays AI Ethics The principles and practices for developing and deploying AI in ways that are fair, transparent, and safe Auditing a hiring algorithm to ensure it doesn't discriminate by gender or race AI Safety The field dedicated to ensuring AI systems remain reliable, controllable, and beneficial as they grow more capable Research into preventing AI from pursuing goals that harm people Alignment The challenge of ensuring an AI system's goals and behaviour match what its designers and users actually intend Preventing a powerful AI from optimising for a metric in a way that causes unintended harm Annotation The process of labelling raw data so it can be used to train supervised learning models Humans drawing bounding boxes around cars in images to train a self-driving model API (Application Programming Interface) A defined interface that lets software systems communicate with each other Calling the OpenAI API to

Two years ago, when I got my first freelance client, I was still in my final semester of college. A...

I am responsible for a research project that is supposed to train a GPT-like model (Transformer-decoder) with 100M, 250M and 500M model variants. # params ## training dataset - 750M tokens - vocabulary is ~15k to ~100k tokens (depends on tokenizer settings) - ~3% of the vocabulary is used in ~50% of the training tokens (similar to language, where most of the vocabulary is used very sparsely) ## training hyper-params - optimizer = AdamW - lr = 1e-3 (works the best compared to 1e-2 and 1e-4) - betas = [0.9, 0.95] - effective batch size = 4M tokens - epoch = 16 - warmup steps ~200 (approx 1 epoch) ## model hyper-params - 16 layers (but variants with up to 48 layers were tested) - embedding = flexible to yield 100M, 250M and 500M model - MLP size = 4*n_embd - 16 attention heads - context window = 1000 # Issue The model seems to fail to learn the basic auto-regressive behavior. It often gets stuck on generating a single token (no repetition penalty, no sampling yet). Is training GPT-like models still a black magic? Is there some trick to this? *Disclaimer*: I will add/edit the parameters above as people ask clarifying questions. submitted by /u/gartin336 [link] [留言]

Blog post: https://wezteoh.github.io/posts/diffusion-for-sketch-guided-trajectory-simulation/ During NBA games, coaches often sketch attacking plays on a whiteboard and mentally simulate how teammates and defenders might react. In this project, I explored using diffusion models for controllable basketball trajectory simulation. Instead of only forecasting future trajectories, the model generates gameplay conditioned on partial “sketches” of player movement instructions. One interesting aspect is that diffusion models refine all player trajectories jointly, which makes sketch-conditioned simulation feel more natural compared to autoregressive generation. I wrote up the methodology, experiments, and implementation details in the link above. Code and model are fully open sourced as well. Curious to hear thoughts from others working on generative modeling, trajectory prediction, or sports analytics. submitted by /u/part-time-delver [link] [留言]

I'm hoping for some advice from any former PhD's outside of machine learning. If you made it into machine learning engineering and/or data science, what was the key for you? Any tips for this job market? It seems like non computer science PhD's are especially in trouble at the moment. submitted by /u/Electrical_Fan_9587 [link] [留言]

[R] BEAM 100K memory benchmark: CSM vs Hindsight local artifact comparison I’m looking for feedback on a local agent-memory benchmark comparison, especially from people who care about evaluation methodology. I built an open-source R&D memory system called Context Swarm Memory (CSM). It uses bounded read-only memory shards, query routing, probe/recall/synthesis, cited packets, and explicit Committer-gated writes. The current comparison is against the accepted local Hindsight artifact on BEAM 100K: CSM: 0.757573 AMB score, 342 / 400 correct Hindsight: 0.733658 AMB score, 326 / 400 correct CSM uses 38.2% fewer answer-visible context tokens CSM is slower: 29.23s average retrieval vs 6.38s I want to be precise about the claim: This is not an official leaderboard claim. It is not a BEAM 10M claim. It is a committed local accepted-artifact comparison at 100K, and the next step should be independent replication or official chart acceptance. Repo: https://github.com/muhamadjawdatsalemalakoum/context-swarm-memory Evidence and reproducibility notes: https://muhamadjawdatsalemalakoum.github.io/context-swarm-memory/ The main question: what would make this comparison scientifically stronger before it is presented as a serious agent-memory result? submitted by /u/keonakoum [link] [留言]

New preprint. A Mixture-of-Experts inference kernel (TritonMoE) written entirely in OpenAI Triton, targeting portability across NVIDIA and AMD without vendor-specific code. Highlights: A fused gate+up GEMM computes both SwiGLU projections from shared tile loads, eliminating 35% of global memory traffic. 89-131% of Megablocks throughput at inference batch sizes (up to 512 tokens) on A100; the same kernel runs on MI300X unchanged. Limitations: falls behind at 2048+ tokens, and degrades with 64+ experts under extreme routing skew. Paper: https://arxiv.org/abs/2605.23911 Code: https://github.com/bassrehab/triton-kernels Writeup with benchmarks: https://subhadipmitra.com/blog/2026/fused-moe-dispatch-triton/ submitted by /u/bassrehab [link] [留言]

Dataset for fine-tuning compliance assistants. Each pair includes: - A practical SME-facing question ("Can I use pre-ticked consent boxes?") - An answer with specific UK GDPR article references, ICO guidance by name, and actionable steps - Source metadata: which GDPR concepts were used, which generation strategy, timestamp Generation method: questions via local Qwen 14B from a curated term bank, answers via DeepSeek API for factual reliability. JSON + Parquet, MIT license for the 1K sample. This is a niche dataset — it's not a benchmark contender, it's for people building privacy tools for UK businesses. If you're doing legal NLP or compliance RAG, might be useful. Free sample: https://huggingface.co/datasets/Draeg82/uk-gdpr-small-business-qa submitted by /u/a_serial_hobbyist_ [link] [留言]

I am a junior in college, and have two accepted workshop papers at ICML 2026. Some background: I had an accepted workshop paper last year at ICLR, but couldn't attend due to a rejected visa, which led to all the more disappointment. So this year I was VERY eager to attend, and my supervisor really wants me to as well. However, the cost of attending (workshop pass, air tickets, etc.) is SO HIGH. Even if my university does offer to cover some of it, it's not gonna cover even half the cost. I'll have to fund it myself. I study for free at my current institution, so my parents wouldn't be mad about paying, but I'm also not someone comfortable asking my parents to pay (which is why I chose my current institution in the first place). So, as third year undergraduate student aiming for grad school, will presenting at ICML workshops/ attending the event have any particular benefits? There's still a part of me that really wants to experience this event, but the cost is going to be a burden. Is it worth it for a 2-day trip? Any insights, experiences, thoughts are welcome. What would you have done? submitted by /u/milasonder [link] [留言]

Each brain is unique, and from the best generations that I save, a worm can pick random brain files to use, letting each worm be completely unique and feel alive. This is for Bonk Universe. submitted by /u/Lanse012 [link] [留言]

. https://x.com/ethanCaballero/status/2059686905105563907 . submitted by /u/Glittering_Author_81 [link] [留言]

I recently wanted to see whether an AI agent could self-improve a harness to solve terminal bench tasks. It’s possible for an AI agent to propose a meaningful one-time change to the harness, but after experimenting with this for a couple of weeks, I think the continuous self-improvement is mostly an experiment-systems problem. The system needs a way to decide what kind of improvements can safely compound. Turns out there's a lot of parallels to coding-agent customization (e.g. SKILLS.md etc..) too. I wrote my experience of building such system here, including the successful and failure attempts during the process, and how I approached the self-improvement loop. It's not intended as a benchmark claim but more of a systems/research writeup. https://www.henrypan.com/blog/2026-05-25-self-improvement-harness/ submitted by /u/Megadragon9 [link] [留言]

Last month NVIDIA released SOL-ExecBench , a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The kernel had passed the benchmark's verifier with room to spare. But in our training run, the loss diverged and never recovered. We started debugging. Replace the dataset distribution with uniformly sampled tokens, the divergence vanishes. Swap SGD for AdamW, also vanishes. This is the worst kind of bug for research. Symptoms and masks both look exactly like "the idea didn't work". It's the type of bug that can make researchers spend a long time debugging without knowing what's at fault: the dataset? the research idea? the architecture? or the implementation itself? Turns out, the actual bug is that the embedding-gradient half of the kernel accumulates in bf16 instead of fp32. Embedding backward sums many small gradient contributions into each token's row of the embedding matrix. With uniform random tokens the contributions spread evenly and bf16 precision is enough. In real text, a handful of token IDs end up with thousands of contributions: the small ones round to zero against the growing accumulator, and the high-frequency rows drift. AdamW's per-parameter normalization absorbs the resulting multiplicative bias, so under AdamW the same drift is invisible in the loss. The other broken submissions had different bug shapes (all interesting). More examples in our blogpost . submitted by /u/laginimaineb [link] [留言]

I'm working on a project that translates any language into English. So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2. The main issue is that they struggle with proper nouns such as: - names - places - dates - organizations I also tried LLMs like Gemma 4, Qwen 3 4B, and Aya Tiny Global, but the issue still persists. The LLMs sometimes partially translate or modify entity names as well. I even tried NER masking / placeholder replacement before translation, but multilingual NER itself becomes a bottleneck. Most NER models only work reliably for a limited set of languages, while my dataset contains 100+ languages, including many low-resource ones. How do production systems usually handle this problem? Are there better multilingual translation models, multilingual NER approaches, or decoding techniques for preserving entities properly? Requirements: - Support for 100+ languages - Runs locally on an RTX GPU - Model size under 7B - English is always the target language. submitted by /u/Illustrious_Age_2792 [link] [留言]

I built a PINN implementation in Python to solve two problems as part of a physics exam project: the damped harmonic oscillator (2nd-order ODE) and the 1D viscid Burgers' equation (nonlinear PDE). Both forward and inverse problems (to estimate unknown equation parameters from data) are implemented for each problem. The repo includes source code, sample outputs, and the written exam report (PDF). Beyond the standard PINN training setup, I ran a comparison against non-physics-informed baselines and specifically investigated extrapolation behavior, i.e. how well the models generalize outside the training domain, and finally made statistical analyses of the parameter estimation performance. GitHub: https://github.com/desdb6/pinn-dho-burgers Ready-to-run demo scripts are included, and the modules are structured to be importable so you can write your own training scripts for more customization. This is not novel research, just a clean student implementation, but hopefully useful to others learning about PINNs. Happy to answer questions or receive feedback in the comments. submitted by /u/Reversed456 [link] [留言]

If you've ever tried to pick an STT vendor for a phone-based voice agent or call center product, you've probably hit this wall: you have plenty of real production audio, but it's unlabeled, so you can't compute WER on it. And the annotated public datasets (FLEURS, CommonVoice, LibriSpeech) are clean studio recordings that have nothing to do with how STT models actually handle your G.711 encoded noisy phone calls. Annotating production audio is slow, expensive, and usually a privacy headache. So most teams end up benchmarking on clean data, picking a vendor, then discovering in prod which one actually survives noise. noisekit fills that gap. Take a clean annotated dataset, apply degradations that approximate your production conditions, end up with a noisy annotated corpus you can run WER on across every STT candidate. uvx noisekit generate \ --dataset google/fleurs --config en_us --split test \ --samples 100 \ --output ./noisy-fleurs Feed ./noisy-fleurs through each STT candidate, normalize, and compute WER with the existing transcripts. The output is HuggingFace AudioFolder-compatible, so load_dataset("audiofolder", data_dir="./noisy-fleurs") works. Presets cover the conditions that actually matter for voice products: telecom: G.711 narrowband bandpass + 8-bit BitCrush + 16-32 kbps MP3 (sounds like a real phone call, not a synthetic low-pass filter) noise: real ambient mixed at 5-15 dB SNR (auto-downloads a MUSAN noise-only subset, or bring your own --noise-dir matching your domain: call center, cafe, car, street) reverb: pyroomacoustics far-field at 1-3 m mic distance low_bitrate: wideband MP3 at 16-32 kbps clipping: ADC / mic saturation clean_reference: control / WER floor compound chains stack realistically. noise_telecom = noisy room then phone codec, which is what an actual support call sounds like. Each output gets PESQ, SNR and NISQA scores in metadata.jsonl alongside the original transcript, so you can correlate WER with measured signal quality after the fac

Vision Transformers waste 90% of their compute recalculating stationary asphalt. NeuroFlow tracks semantic surprise in embedding space, physically eliminating background tokens before the encoder. Result: 55.8x wall-clock speedup for ViTs on high-res video (1792p) with 97% fidelity. No fine-tuning required. NeuroFlow is a dynamic routing framework for Vision Transformer video inference. It exploits temporal redundancy by tracking per-patch semantic surprise via an Exponential Moving Average (EMA) of patch-level embeddings, effectively answering the architectural mismatch between O(N2) self-attention and highly redundant natural video streams. Key Contributions Architecture C (Dual-Memory Reconstruction): A completely training-free inference engine that combines a Layer 0 Retinal Gate with a Layer 12 Cortical Cache. It achieves 71.55% zero-shot top-1 accuracy at 84.0% token sparsity on SigLIP, retaining 92.4% of dense accuracy without modifying any weights. Architecture B (Extreme Wall-Clock Speedup): Physically eliminates stationary tokens before the encoder. With sparse manifold distillation, it reduces 1792p SigLIP 2 inference from 678 ms to 11.9 ms—a 55.80× wall-clock speedup at 97.37% embedding fidelity. LLM Ablation: Characterises the architectural boundaries of applying similarity-gated bypass to autoregressive language models (Phi-3-mini), demonstrating 0% token drift in syntactically constrained generation. Code and paper: https://github.com/ynnk-research/-NeuroFlow submitted by /u/Bobby-Ly [link] [留言]