model-agnostic sensitivity approximator [P]

<!-- SC_OFF --><div class="md"><p>Small MLP (2 layers × 256 units, ~85 KB) that accurately predicts 58 <a href="https://github.com/naver/anny">Anny</a> body-shape parameters from

<!-- SC_OFF --><div class="md"><p>Link to the paper:</p> <p><a

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>We’re developing TanML, an MIT-licensed automated model-validation toolkit for tabular machine-learning models.</p> <p>TanML runs locally and

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’ve been building an end-to-end Air Quality (PM2.5) forecasting pipeline for 4 countries (US, UK, India, Australia) using

<!-- SC_OFF --><div class="md"><p>I built a calibrated predictive model on point-in-time fundamentals and two results surprised me enough to write up. Sharing the project and the

<!-- SC_OFF --><div class="md"><p>After burning way too much money on GPT-4 API calls, I finally went all-in on local models with Ollama. The progress in 2026 is insane

<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1sw0vcf/stanford_researchers_fed_a_language_model_a_dna/"> <img alt="Stanford researchers fed a language model a