Lecture - Theories of Deep Learning MT25, II, Why deep learning
Papers mentioned
- ImageNet Large Scale Visual Recognition Challenge
- ImageNet Classification with Deep Convolutional Neural Networks, Krizhevsky
- [[Paper - Mastering the Game of Go with Deep Neural Networks and Tree Search]]N
- Image Style Transfer Using Convolutional Neural Networks, Gatys
- Visually Indicated Sounds, Owens
- Zero-Shot Text-to-Image Generation
- CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
- Highly accurate protein structure prediction with AlphaFold
- Skilful precipitation nowcasting using deep generative models of radar
- Advancing mathematics by guiding human intuition with AI
- A Deep Learning Approach to Antibiotic Discovery
- [[Article - Deep, deep trouble, Elad]]U
- [[Paper - Representation Benefits of Deep Feedforward Networks, Telgarsky (2015)]]U