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#autoencoder

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01 ·Video Codecs·★ MEMBER·PAPER·11 min read Neural Compression — The Codec That Learns JPEG's quantization table and H.264's prediction modes were tables people worked out by hand. Neural compression hands that design job to the data instead. The two pillars — an autoencoder and an entropy model — the trick that gets around non-differentiable rounding, the hyperprior, and what standards bodies are actually arguing about on the road to JPEG AI. 02 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: AURORA-LM — A Diffusion Language Model That Keeps Its Text Latent Full-Width Instead of Compressing It A walkthrough of AURORA-LM, a continuous-latent diffusion language model that rejects the usual compromise of compressing text latents to make diffusion easier. It keeps a decodable, width-1024 latent and learns its distribution with a low-rank input pathway, width-calibrated noise, and self-trajectory consistency. 03 ·CNNs & Image Recognition·★ MEMBER·11 min read Anomaly Detection from Scratch — Learning From Normal Alone Why an AI can learn from good units alone on a factory floor where defect samples barely exist. Two rulers — reconstruction error and density estimation — built up from metaphor to equations, all the way to the part that decides everything in practice: choosing the threshold.