David J. Schwab

1. An exact mapping between the Variational Renormalization Group and Deep\n Learning
   2014/10/14 by Pankaj Mehta, David J. Schwab, Mehta, Pankaj +1 · 8 voices · 17 citations
   Physics and Astronomy · Materials Science · #Quantum many-body systems #Machine Learning in Materials Science #Statistical Mechanics and Entropy
2. A high-bias, low-variance introduction to Machine Learning for physicists
   2018/03/23 by Pankaj Mehta, Marin Bukov, Ching-Hao Wang +5 · 3 voices · 14 citations
   Computer Science · Materials Science · #Computational Physics and Python Applications #Gaussian Processes and Bayesian Inference #Machine Learning in Materials Science #cond-mat.stat-mech #cs.LG #physics.comp-ph #stat.ML
3. Energetic costs of cellular computation
   2012/03/24 by Pankaj Mehta, David J. Schwab · 6 voices · 3 citations
   Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Engineering · #Gene Regulatory Network Analysis #Advanced Thermodynamics and Statistical Mechanics #Molecular Communication and Nanonetworks
4. The Early Phase of Neural Network Training
   2020/02/24 by Jonathan Frankle, David J. Schwab, Frankle, Jonathan +3 · 1 voice · 8 citations
   Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Domain Adaptation and Few-Shot Learning #Stochastic Gradient Optimization Techniques #cs.LG #cs.NE #stat.ML
5. Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
   2020/02/29 by Jonathan Frankle, David J. Schwab, Frankle, Jonathan +3 · 1 voice · 5 citations
   #cs.LG #cs.AI #cs.NE #stat.ML
6. The Deterministic Information Bottleneck
   2017/06/01 by DJ Strouse, David J. Schwab · 5 citations
7. Seasonal and interannual effects of hypoxia on fish habitat quality in central Lake Erie
   2010/09/30 by Kristin K. Arend, KRISTIN K. AREND, Dmitry Beletsky +15 · 3 citations
   Environmental Science · #Fish Ecology and Management Studies #Marine and fisheries research #Aquatic Invertebrate Ecology and Behavior
8. Learning Optimal Representations with the Decodable Information Bottleneck
   2020/09/27 by Yann Dubois, Dubois, Yann, Douwe Kiela +5 · 4 citations
   Computer Science · #Adversarial Robustness in Machine Learning #Domain Adaptation and Few-Shot Learning #Machine Learning and Algorithms
9. Learning to Share and Hide Intentions using Information Regularization
   2018/08/06 by Daniel Strouse, Strouse, DJ, Max Kleiman‐Weiner +7 · 5 citations
   Computer Science · Social Sciences · Neuroscience · #Reinforcement Learning in Robotics #Experimental Behavioral Economics Studies #Neural dynamics and brain function
10. How noise affects the Hessian spectrum in overparameterized neural networks
    2019/10/01 by Mingwei Wei, Wei, Mingwei, David J. Schwab +1 · 2 citations
    Computer Science · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Stochastic Gradient Optimization Techniques
11. Evolution of a cyanobacterial bloom forecast system in western Lake Erie: Development and initial evaluation
    2013/01/01 by Timothy T. Wynne, Richard P. Stumpf, Michelle C. Tomlinson +4 · 1 citation
    Earth and Planetary Sciences · Environmental Science · #Aquatic Ecosystems and Phytoplankton Dynamics #Marine and coastal ecosystems #Water Quality and Pollution Assessment
12. When can in-context learning generalize out of task distribution?
    2025/06/05 by Chase Goddard, Lindsay M. Smith, Goddard, Chase +5 · 3 citations
    Computer Science · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #Domain Adaptation and Few-Shot Learning #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC) #Statistical Mechanics (cond-mat.stat-mech) #Stochastic Gradient Optimization Techniques