Daniel A. Roberts

1. The Principles of Deep Learning Theory
   2021/06/18 by Daniel A. Roberts, Sho Yaida, Boris Hanin · 11 voices · 13 citations
   Computer Science · Physics and Astronomy · #Neural Networks and Applications #Anomaly Detection Techniques and Applications #Statistical Mechanics and Entropy
2. Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
   2024/04/01 by Matthias Gerstgrasser, Rylan Schaeffer, Gerstgrasser, Matthias +26 · 16 voices · 31 citations
   Computer Science · #Semantic Web and Ontologies #cs.AI #cs.CL #cs.ET #cs.LG #stat.ML
3. Why is AI hard and Physics simple?
   2021/03/31 by Daniel A. Roberts, Roberts, Daniel A. · 4 voices · 1 citation
   Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Statistical Mechanics and Entropy #Gaussian Processes and Bayesian Inference
4. The Unreasonable Ineffectiveness of the Deeper Layers
   2024/03/26 by Andrey Gromov, Kushal Tirumala, Gromov, Andrey +7 · 4 voices · 32 citations
   Computer Science · #Advanced Neural Network Applications #Multimodal Machine Learning Applications #Topic Modeling #cs.CL #cs.LG #stat.ML
5. Complexity Equals Action
   2015/09/25 by Adam R. Brown, Daniel A. Roberts, Leonard Susskind +2 · 1 voice · 52 citations
   Physics and Astronomy · #Black Holes and Theoretical Physics #Noncommutative and Quantum Gravity Theories #Quantum many-body systems #hep-th
6. Gradient Descent Happens in a Tiny Subspace
   2018/12/12 by Guy Gur-Ari, Daniel A. Roberts, Gur-Ari, Guy +3 · 35 citations
   Computer Science · #Stochastic Gradient Optimization Techniques #Gaussian Processes and Bayesian Inference #Domain Adaptation and Few-Shot Learning
7. Feature Learning and Generalization in Deep Networks with Orthogonal Weights
   2023/10/11 by H. Michael Day, Day, Hannah, Yonatan Kahn +3 · 2 citations
   Computer Science · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High Energy Physics - Theory (hep-th) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications