What is a Random Number and Why Should I Care - Understanding Pseudorandom Number Generators
Explore random number generation, distributions, and applications in C++. Learn about PRNGs, chaos theory, and visualize concepts through blob racing simulations.
DeepParticle: Learning Invariant Measure by Deep Neural Network Minimizing Wasserstein Distance
Explore DeepParticle: an integrated approach combining deep learning, optimal transport, and interacting particles to solve high-dimensional PDEs, demonstrated through Fisher-Kolmogorov-Petrovsky-Piskunov front speeds in incompressible flows.
A Lévy-Khintchine Formula for SUq(N)
Explore the Lévy-Khintchine formula for SUq(N) in quantum mathematics, delving into advanced concepts and their applications in mathematical physics.
Random Tensor Networks with Nontrivial Links
Explorez les réseaux de tenseurs aléatoires avec états de liens non triviaux, leurs propriétés d'intrication et applications en théorie de l'information quantique et gravité quantique holographique.
Geometry of Dimer Models - ICBS 2024
Explore asymptotic phenomena in random dimer coverings, focusing on Aztec diamonds and their connection to Riemann surface geometry.
Geometry of Dimer Models - ICBS 2024
Explore asymptotic phenomena in random dimer coverings, focusing on Aztec diamonds and their connection to Riemann surface geometry.
Centering the Everett Interpretation - Probability in Quantum Mechanics
Explore the Everett interpretation of quantum mechanics, focusing on probability as objective chances of centered propositions. Gain insights into fundamental physical theories.
A New Formulation of Quantum Theory
Explore a novel correspondence between stochastic processes and quantum theory, offering new insights into quantum mechanics and potential applications for quantum computing.
Conquering Mount Everett: Branch-Counting Versus the Born Rule
Explore quantum branching scenarios and self-locating credences through Indexed Branch-Counting, challenging Everettian approaches and the Born Rule in quantum mechanics.
Diffusion Models in Machine Learning - Lecture 16
Explore diffusion models in machine learning, covering theory, applications, and efficient implementation techniques for advanced AI image generation.
Diffusion Models in Efficient Machine Learning - Lecture 16
Explore diffusion models in-depth, covering theory, applications, and efficient implementation techniques for advanced machine learning practitioners.
The Entropic Dynamics Approach to Quantum Mechanics
Explore the Entropic Dynamics framework for reconstructing Quantum Mechanics, focusing on ontic and epistemic variables, probability distributions, and the derivation of key quantum principles.
Data-Driven Latent Representations for Time-Dependent Problems - Lecture 3
Explore data-driven latent representations for time-dependent problems, focusing on applications in climate downscaling, superresolution, and optimal transport.
Randomness and Complexity - Lecture 1
Explore the intersection of randomness and complexity in mathematics, delving into key concepts and their applications in discrete mathematics and computer science.
Wasserstein Gradient Flows and Applications to Sampling in Machine Learning - Lecture 1
Explore Wasserstein gradient flows and their applications to sampling in machine learning through an in-depth lecture by Anna Korba.
Wasserstein Gradient Flows and Applications to Sampling in Machine Learning - Lecture 3
Explore Wasserstein gradient flows and their applications to sampling in machine learning with Anna Korba's advanced lecture on this complex mathematical topic.
2D Percolation Revisited
Explore 2D percolation theory with Fields Medalist Stanislav Smirnov in this IMPA 70th anniversary conference talk. Gain insights into advanced mathematical concepts and recent developments in the field.
Statistical Network Analysis - Course 2
Explore statistical network analysis techniques with experts from Texas A&M and Wisconsin. Gain insights into discrete probability, collaborate on open problems, and advance your research in this month-long program.
Statistical Network Analysis - Course 2
Explore statistical network analysis techniques with experts from Texas A&M and Wisconsin. Gain insights into discrete probability, collaborate on open problems, and advance your research in this month-long program.
Statistical Network Analysis - Course 2
Explore statistical network analysis techniques with experts from Texas A&M and Wisconsin. Gain insights into discrete probability, collaborate on open problems, and advance your research in this month-long program.
Wigner's Semicircle Distribution as Limit Distribution
Explore the Wigner distribution as a limit of eigenvalue distributions in random matrices, with a focus on probability theory and mathematical analysis.