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 School of Engineering. Based on th ...
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 real-time demonstration of learning Adaptable World Models with Latent Actions using the Agno framework.

The development of this repository was inspired by the paper "AdaWorld: Learning Adaptable World Models with Latent Actions". To read the entire paper, visit https ...
GitHub

A comprehensive Python framework designed for exploring the loss landscapes of deep learning models.

Landscaper is available on PyPI, making it easy to install and integrate into your projects. @misc{https://doi.org/10.5281/zenodo.15874987, doi = {10.5281/ZENODO ...
IEEE

Model-Agnostic Empirical Evaluation of Test-Driven Prompt Engineering on Improving Accuracy and Efficiency in Large Language Models Python Code Generation

Abstract: Although Large Language Models (LLMs) are widely adopted for Python code generation, the generated code can be semantically incorrect, requiring iterations of evaluation and refinement. Test ...