AI Agents Run Experiments While You Sleep, So What Should Knowledge Workers Do?

Karpathy's autoresearch and the cognitive labor displacement thesis converge on the same conclusion: the scientific method is ...
Analytics Insight

Deep Generative Models, Stanford University

Stanford University’s Deep Generative Models (XCS236) is a graduate-level, professional online course offered by the Stanford ...
IEEE

Autoregressive models for fast breathing rate estimation with distributed elevated radars

Abstract: The problem of contactless accurate monitoring of vital signs of patients in psychiatric settings is addressed in this work. Most state-of-the-art radar-based approaches rely on measurements ...
IEEE

3D-SLARM: Practical Lossless Volumetric Image Compression via a 3D-Scanning Lightweight Autoregressive Model

Abstract: Volumetric images often encapsulate critical information, making it essential to employ lossless compression to preserve data integrity. Although various learned methods have demonstrated ...
GitHub

A model-driven approach to building AI agents in just a few lines of code.

Strands Agents is a simple yet powerful SDK that takes a model-driven approach to building and running AI agents. From simple conversational assistants to complex autonomous workflows, from local ...
GitHub

GitHub - kossisoroyce/timber: Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code ...

Timber takes a trained ML model — XGBoost, LightGBM, scikit-learn, CatBoost, ONNX (tree ensembles, linear models, SVMs), or a URDF robot description — runs it through a multi-pass optimizing compiler, ...