Emergent Second Law in Macroscopic Stochastic Thermodynamics - Part 2
Explore macroscopic stochastic thermodynamics and the emergent Second Law with Nahuel Freitas in this advanced physics seminar from ICTP-SAIFR.
Emergent Second Law in Macroscopic Stochastic Thermodynamics - Part 1
Explore macroscopic stochastic thermodynamics and the emergent Second Law with Nahuel Freitas in this advanced physics seminar from ICTP-SAIFR.
Learning Probability Distributions of Neuronal Activity Patterns - Max Entropy Models
Explore max entropy models for understanding neuronal activity patterns and probability distributions in biological systems.
Amplitudes and Applications - Class 5
Explore advanced concepts in particle physics, focusing on amplitudes and their applications in the final session of a comprehensive series.
Time Series Analysis Tools with Applications to Neuroscience - Class 3
Explore advanced time series analysis techniques and their applications in neuroscience, focusing on complex network analysis and information theory.
Driven Quantum Dynamics III - Lecture 3
Explore quantum dynamics, focusing on localization, Fourier expansion, and chaos. Gain insights into stable localization, delocalization, and Anderson localization.
Thermalization and Many-Body Localization - Lecture 4
Explore advanced concepts in thermalization and many-body localization with David Huse's in-depth lecture, part of the BSS2023 Physics School series.
Thermalization and Many-Body Localization - Part III
Explore advanced concepts in thermalization and many-body localization with David Huse, delving into cutting-edge physics research and theories.
Mean Field Limit in Neural Network Learning
Explore the mean field limit in neural network learning, examining optimization dynamics, convergence to global optima, and data representation in autoencoders. Gain insights from physics analogies.
Supervised Quantile Normalization for Matrix Factorization Using Optimal Transport
Explore advanced matrix factorization techniques using optimal transport for quantile normalization. Learn applications in dimensionality reduction and genomics from expert Marco Cuturi.
Benign Overfitting in Linear Regression and Deep Learning
Explore benign overfitting in deep learning with Peter Bartlett. Uncover insights on linear regression, effective rank, and overparameterization in neural networks for optimal prediction accuracy.
Functional Gradient Descent Methods for Optimization and Sampling
Explore advanced gradient descent techniques for complex optimization and sampling problems in machine learning, including Stein variational gradient descent and neural architecture optimization.
Data Cleaning with MATLAB - nanoHUB Tech Talk
Learn essential techniques for data cleaning in MATLAB, including handling missing values, removing outliers, and formatting data for analysis. Enhance your research and data processing skills.
Stochastic Models for Tumor Control Probability and Probability of Cure - Part 1
Explore stochastic modeling techniques for analyzing tumor control and cure probabilities, focusing on mathematical approaches in oncology research and treatment outcomes.
Stochastic Resetting
Delve into stochastic resetting theory through an advanced physics lecture exploring fundamental concepts, mathematical frameworks, and real-world applications in complex systems and random processes.
Frontiers in Statistical Physics - Session 3
Explore advanced concepts in statistical physics through expert discussions and theoretical frameworks examining frontier developments in the field.
New Paradigms in Nano Heat Engines
Explore groundbreaking research in single-particle heat engines, their mesoscopic behavior, and innovative approaches to enhance efficiency through engineered system-bath interactions and bacterial reservoirs.
Geometry-oriented Measures of Dependence in Statistical Analysis
Explore geometric approaches to measuring statistical dependence, examining axioms and algorithms for evaluating relationships between variables while comparing classical and modern dependence measures.
Towards Reverse Algorithmic Engineering of Neural Networks
Explore the algorithmic decision-making process of neural networks through NeuroSAT case study, examining how ML models learn and apply combinatorial features and confidence-based variable selection in SAT solving.
Breaking Black-Box AI
Explore failure cases of popular AI algorithms through live demos, from linear regression to deep learning. Learn to identify and address silent failures in black-box AI systems.
Foundations of Type-Driven Probabilistic Modelling
Explore type-driven abstractions for probabilistic modelling, covering discrete probability and quasi-Borel spaces. Gain hands-on experience through accompanying exercises for self-study.