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If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:
The setup is roughly: humans take recent work from top-tier ML/AI conferences, turn part of it into a well-defined task with an evaluator, and then let an agent iteratively modify the solution and search for a better score.
When machines learn by themselves The post Introduction to Reinforcement Learning: Multi-Armed Bandit Simulation in Python appeared first on Towards Data Science.
I put a physics-informed neural network up against a plain finite-difference solver twice: once in 1D and once in 5D. The winner changed. The post When Do PINNs Beat Classical Numerical Methods? A 1D vs 5D Experiment appeared first on Towards Data Science.
Hi All, Does anyone know when is the deadline to fill in event metadata? (the screenshot below) Is it the same time as camera-ready? Or there is a deadline for it? And is everyone's topic None?
I’m hitting rate limits on Together AI. For context, I’ve been working on an agentic repository indexing and benchmark generation tool, and I’m running multiple agents in parallel across models like Llama 3.3 70B and Qwen 2.5.
https://preview.redd.it/t7wxguxqvzth1.png?width=1314&format=png&auto=webp&s=0ba382fb88894c6e687d5bb9a4b14a85334eae67 Guess we have a dataset for sycophancy and AI content detection submitted by /u/Striking-Warning9533 [link] [comments]
Meta has open-sourced Rebalancer, the C++ and Python library it has used for over 9 years to place shards, servers and traffic. It handles about 40 million assignment problems a day, using local search or MIP solvers like Gurobi, FICO Xpress and HiGHS. It is pip-installable under Apache 2.0.
I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some point. Learn python -> Learn ML & Maths -> Read other research papers -> find a topic ?
What do they tell us about a linear transformation?Continue reading on Towards AI »
Why 80% of enterprise RAG failures happen before retrieval starts. Learn the Goldilocks chunking dilemma, 512-token truncation, and production splitting rules.
Complete RAG Engineering Series, Part 6 | A practical guide to HNSW, IVF, product quantization, FAISS, Pinecone, Weaviate, Qdrant, Milvus…Continue reading on Towards AI »
What each paper really says, why it still matters, and what to read next.Continue reading on Towards AI »
Explore a comprehensive coding guide to Laya, the open-source zero-shot decision engine. Learn how to implement typed decisions, fit custom temperatures, and build reliable abstention gates using real-world CLINC150 banking data.
For NeurIPS, I already registered a few months ago (for Atlanta) when there were news that Sydney is sold out. However, I forgot to register for the workshop. Now, when I go to register just for the workshop, it just says that
I know top ML PhD admissions are insanely competitive, so I’m trying to figure out if it’s even worth applying or if I should just focus seriously on jobs instead.
Hi ML Community, I am excited to share our latest framework, AFP-GIC, officially published in IEEE Access (2026). We have released the deployment codebase and hosted an interactive visual playground.
Mistral AI has released Mistral Large 4, nicknamed Le Chonk, as a public preview. It is a 1.05 trillion parameter Mixture of Experts model with 49 billion active parameters, native image input, and a 1 million token context window, trained on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own…
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