Learned Codecs, AI Compression, Rate-Distortion Theory, Entropy Models
I spent weeks understanding Netflix's recommendation system - here's what I learned (Matrix Factorization breakdown + working code)
beyondit.blog·1d
How Amazon scaled Rufus by building multi-node inference using AWS Trainium chips and vLLM
aws.amazon.com·22h
ParallelSearch: Train your LLMs to Decompose Query and Search Sub-queries in Parallel with Reinforcement Learning
arxiv.org·12h
SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Images
arxiv.org·12h
Deep and diverse population synthesis for multi-person households using generative models
arxiv.org·12h
Arm neural technology to adds AI acceleration to Arm GPUs, enables “Neural Super Sampling” for lower bandwidth/higher FPS
cnx-software.com·1d
SOFA: Deep Learning Framework for Simulating and Optimizing Atrial Fibrillation Ablation
arxiv.org·2d
TAR-TVG: Enhancing VLMs with Timestamp Anchor-Constrained Reasoning for Temporal Video Grounding
arxiv.org·2d
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