All the articles with the tag "machine-learning".
Why video is harder than images, the DiT revolution for video, and how diffusion models learn temporal consistency. Covers V-DiT, AsymmDiT, and the mathematical foundations of video generation.
A deep dive into how datasets and dataloaders power modern AI—from the quiet pipeline that feeds models to the sophisticated tools that make training efficient. Understanding the hidden engine that keeps AI systems running.
The hardest problem in AV: predicting human irrationality. Covers the evolution from physics-based prediction to Generative AI, tracking the journey through Waymo Open Dataset Challenges.
A deep dive into physics-aware video diffusion models: how researchers inject physical constraints into generative models, the three leading technical approaches, and their practical impact on robotics and scientific simulation.
An intuitive introduction to the Transformer architecture — from the attention mechanism to self-attention and cross-attention, using language translation as a concrete example.
An intuitive introduction to Variational Autoencoders — how compressing data into probabilistic codes enables machines to generate realistic images, sounds, and structures.