Seminar Summary of "Optimization in Machine Learning"
The summary of the seminar “Optimization in Machine Learning”, covering Bayesian Optimization, multi-fidelity methods, handling discrete search spaces, and the BANANAS method for NAS.
Posts
BANANAS
Paper Reading Notes: “BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search”
UrbanLF
Paper Reading Notes: “UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes”
Large Concept Models
Paper Reading Notes: “Large Concept Models: Language Modeling in a Sentence Representation Space”
From Tokens To Thoughts: How LLMs And Humans Trade Compression For Meaning
Paper Reading Notes: “From Tokens To Thoughts: How LLMs And Humans Trade Compression For Meaning”
RT Series (RT-1, RT-2)
Paper Reading Notes: “RT-1: Robotics Transformer for Real-World Control at Scale” and “RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control”
CLIP
Paper Reading Notes: “Learning Transferable Visual Models From Natural Language Supervision”
Diffusion Policy
Paper Reading Notes: “Diffusion Policy: Visuomotor Policy Learning via Action Diffusion”
Synthesizer
Paper Reading Notes: “Synthesizer: Rethinking Self-Attention for Transformer Models”
Learning Transformer Programs
Paper Reading Notes: “Learning Transformer Programs”