359 stories
I passed the Claude Certified Architect — Professional and Claude Certified Developer — Foundations exams. The score came down to…Continue reading on Towards AI »
I'm a first year ML PhD at a T5 ML program in the US. I managed to get a first author paper accepted to NeurIPS Main Track last month and thought I'd start applying for PhD research internships since I saw a lot of them required a "top" venue publication.
I started Flama in 2018 to take a trained model to a production API with as little ceremony as possible. With 2.0 LLMs became first-class alongside classic models, and together with Miguel Durán-Olivencia we've now written up the whole architecture in an 83-page paper on arXiv (2608.18733).
Came across this GitHub project called Paper Review Harness. Thought it might be useful for people preparing ML research papers.
Part 5 of the Production RAG Series — Small-to-Big Retrieval, Bounded Semantic Splitting, Breadcrumbs, and Anthropic’s Contextual Retrieval.
Benchmarking NumPyro, affine-invariant MCMC, and dynesty against exact ground truth and watching them fail. The post Three Samplers, One Posterior: NUTS vs. Emcee vs. Nested Sampling appeared first on Towards Data Science.
https://preview.redd.it/m5e9anluo8vh1.png?width=1080&format=png&auto=webp&s=4c80687e6fe40036843a97ed8975ce46eaab0662 I am excited to share a project I have been working on during the past few years during my free time.
Hi r/MachineLearning, Stjepan from Manning here. The mods approved this post, and I appreciate being welcomed back to share another book with you. We’ve just launched AI Recommender Systems: Transformers, LLMs, agents, and RAG by Kim Falk in MEAP, our early-access program.
Split conformal prediction gives you a clean statement: with a calibration set exchangeable with future data, your prediction sets cover the true label at the rate you chose. The trouble is that production data rarely stays exchangeable.
Z.ai’s GLM-Legal is a legal AI model built on GLM-5.3-Flash. It scores 19.2% all-pass on Harvey’s Legal Agent Benchmark, nearly 3x its base, at $0.15 per test. Access is by application. The post Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB appeared first on MarkTechPost.
This post is to let people know about a workshop/tutorial on Machine Learning that will run on the (blue) math discord server: discord.gg/maths. The place is nice, people are cool and helpful, and as you can see it is a mathematics server, so the learning environment is quite focused indeed.
Loss Functions Deep Dive — Part 4 of 30Continue reading on Towards AI »
What does the adjoint tell us about a linear transformation?Continue reading on Towards AI »
A new model called Jev is built for this kind of job. You give it the ticket and the possible answers, and it returns a choice with a probability, nothing else. A startup called TypeSafe AI released it on September 15.
I’m prioritizing my path and was wondering if an MS is a soft requirement for some of these Research Engineer, Applied Scientist, Post-Training roles. I only have a BS but I work full time as a Data Scientist and was wondering how I should go about this career transition.
learnc tunes named constants in unmodified C code against a score function you write in C, and writes the same file back with new values. Results, both on held-out data: JPEG, stb_image_write.h (128 quantization-table entries, tuned on 12 Kodak images, tested on the other 12): 9.1% BD-rate at equal…
Most conversations I see on various AI subreddits start from the assumption that AGI/RSI/ASI is possible, we're on the way right now, and that things will be incredibly different afterward. The only question is what the post-AGI world looks like.
Paper: Looped Diffusion Transformer (arXiv 2609.40305, 30 Sep). Instead of making a text-to-image DiT bigger, it runs a shared group of transformer blocks several times inside each denoising step.
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