Quantum Collapse Models and Awareness
Exploring quantum collapse models as a key to understanding awareness, linking quantum physics with consciousness and integrated information theory.
Rough Paths for Physicists - Lecture 3
Explore advanced concepts in rough path theory, focusing on applications in physics and stochastic analysis. Gain insights into this powerful mathematical framework.
Rough Paths for Physicists - Lecture 2
Explore advanced mathematical concepts in rough path theory, focusing on applications in physics and stochastic analysis.
Rough Paths for Physicists - Course 2: Rough Paths, Combinatorics and Regularity
Explore rough paths, combinatorics, and regularity in physics, focusing on Brownian motion and its applications in mathematical analysis.
Rough Paths for Physicists - Motivations, Sewing Lemma, and Young Integrals - Lecture 1
Explore rough paths in physics, focusing on motivations, the sewing lemma, and Young integrals. Gain insights into advanced mathematical concepts for physical applications.
Fokker-Planck-Inverse Reinforcement Learning
Explore Fokker-Planck-Inverse Reinforcement Learning for revealing agent behavior rationale. Learn about a novel physics-aware algorithm inferring transition and reward functions from observed trajectories.
Gene Regulatory Network Structure and Perturbation Effects Distribution - Simulation and Analysis
Explore gene regulatory network structure and its impact on perturbation effects. Gain insights into simulating and modeling GRNs for improved understanding of gene expression regulation.
DeepOnet - Learning Nonlinear Operators Based on the Universal Approximation Theorem of Operators
Learn to approximate nonlinear operators using DeepONet, a neural network architecture based on the universal approximation theorem. Explore applications in differential equations, fluid mechanics, and multiphysics problems.
Score Matching via Differentiable Physics
Explore score matching via differentiable physics for inverse problems. Learn about combining physics operators with stochastic differential equations to generate plausible trajectories towards given end states.
Reflected Diffusion Models: Principled Data Constraints in Generative Modeling
Explore reflected diffusion models for improved image generation. Learn about reversing stochastic differential equations, addressing numerical errors, and incorporating data constraints in principled ways.
An Optimal Control Perspective on Diffusion-Based Generative Modeling
Explore optimal control theory's connection to diffusion-based generative modeling. Gain insights into stochastic differential equations, Hamilton-Jacobi-Bellman equations, and novel sampling methods for unnormalized densities.
Transport, Variational Inference and Diffusions
Explore connections between optimal transport and variational inference, focusing on stochastic differential equations and Girsanov transformations. Discover novel sampling and generative modeling techniques.
Action Matching: Learning Stochastic Dynamics from Samples
Explore Action Matching for learning stochastic dynamics from samples. Discover applications in quantum systems, biology, and generative modeling. Gain insights into novel approaches for simulating trajectories.
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions - Lecture
Explore a unified framework for generative models, bridging flow-based and diffusion-based methods using stochastic interpolants. Learn about probability flow equations, likelihood estimation, and applications in AI for drug discovery.
Hessian-Aware Stochastic Differential Equation Modeling of SGD
Explore a novel SDE model for SGD that incorporates Hessian information, improving accuracy in capturing escaping behaviors and achieving exact recovery for quadratic objectives.
A Cookbook for Deep Continuous-Time Predictive Models
Explore deep continuous-time predictive models for irregularly-sampled time series, from feedforward approaches to latent-variable stochastic differential equations.
Animal Growth in a Randomly Varying Environment - An Application Study
Explore animal growth models in random environments, focusing on applications and mathematical analysis of population dynamics and ecology.
Diffusion Models for Inverse Problems
Explore advanced techniques for solving inverse problems using diffusion models, focusing on posterior sampling and manifold constraints.
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.