LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.

The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science.

LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.

The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science.

Waymo has revealed new technical details about the onboard computing system that powers its autonomous driving technology, including a purpose-built 5 nm ASIC delivering more than 1,000 TOPS of machine learning performance. The company says its latest compute architecture is designed to provide millisecond-level response times while operating under the vibration, temperature extremes and other […]

Conceptual overview and walkthrough of a solution approach in Python

The post Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision appeared first on Towards Data Science.

A clear, math-first walkthrough of how VAEs learn to generate new data

The post Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick appeared first on Towards Data Science.

How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on

The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science.

AGIBOT says its WITA-Omni Preview multimodal foundation model has achieved the highest score on the Daily-Omni audio-visual reasoning benchmark, outperforming models from Alibaba, Google and ByteDance. According to the company, WITA-Omni Preview recorded an average accuracy of 85.21 percent on the third-party benchmark, ahead of Qwen3.5-Omni-Plus, Gemini 3.1 Pro Preview and Doubao Seed 2.0 Lite. […]