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PHYS-754: Lecture series on scientific machine learning

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Lectures in this course (76)

Optimal Errors and Phase Transitions in Generalized Linear Models Log in to Mediaspace to watch this video

Covers optimal errors, phase transitions, and algorithm performance in linear models.

Marvels and Pitfalls of the Langevin in Noisy High-Dimensional Inference Log in to Mediaspace to watch this video

Discusses metastability, phase transitions, approximate message passing algorithm limitations, and the efficiency of Langevin dynamics in high-dimensional inference.

Untitled Log in to Mediaspace to watch this video

Neural Network Quantum State Tomography Log in to Mediaspace to watch this video

Explores neural networks for quantum state tomography, focusing on highly-entangled systems, training schemes, and overfitting.

Deep Neural Network Solution of the Electronic Schrödinger Equation Log in to Mediaspace to watch this video

Explores deep neural network solutions for the electronic Schrödinger equation and their computational efficiency in many-body physics.

Neural Tangent Kernel: Generalization in Deep Learning Log in to Mediaspace to watch this video

Explores the neural tangent kernel in deep learning, analyzing generalization and network behavior.

Relative Stability Towards Diffeomorphisms in Deep Nets Log in to Mediaspace to watch this video

Explores the impact of relative stability towards diffeomorphisms in deep neural networks and its correlation with performance.

Learning Sparse Features: Overfitting in Neural Networks Log in to Mediaspace to watch this video

Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.

Chemical Reactions: Transformer Architecture Log in to Mediaspace to watch this video

Explores atom mapping in chemical reactions and the transition to reaction grammar using the transformer architecture.

Predicting Reaction Yields with Deep Learning Log in to Mediaspace to watch this video

Explores predicting reaction yields with deep learning models and the importance of high-quality data sets in chemistry.

AI in Chemistry: Hypergraph and Single-Step Ritual Synthesis Log in to Mediaspace to watch this video

Explores AI applications in chemistry, hypergraphs, single-step ritual synthesis, and retro-synthetic models evaluation.

Modeling the Brain: Machine Learning and Neuroscience Log in to Mediaspace to watch this video

Explores the synergy between machine learning and neuroscience, showcasing how deep neural networks can predict neural responses and the challenges faced by AI in robotics.

Memory Systems and Brain Function Log in to Mediaspace to watch this video

Explores memory systems, brain function measurement methods, and proteomics.

Neural Network Quantum States Log in to Mediaspace to watch this video

Explores neural network quantum states, discussing parameterization, complex valued functions, and training processes.

Protein Co-evolution Analysis Log in to Mediaspace to watch this video

Delves into protein co-evolution analysis, covering discoveries, residue contacts, and direct coupling analysis.

Protein Folding: From DCA to AlphaFold Log in to Mediaspace to watch this video

Explores the evolution of protein folding techniques, from DCA to AlphaFold.

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