Deep Generative Models - Maximum Likelihood Learning - Lecture 4
Explore maximum likelihood learning in deep generative models with Stanford's Stefano Ermon. Gain insights into advanced AI techniques and their applications in this comprehensive lecture.
Using Classical Algorithms for Modern Applications: MCMC for Tail Calculations, EM - Lecture
Explore classical algorithms applied to modern challenges: MCMC for tail probability estimation and EM for PET reconstruction. Gain insights into innovative adaptations of established methods for cutting-edge problems.
Probability via Computation - Week 4
Explore probability concepts through computational methods, covering random sampling, Monte Carlo simulations, Bernoulli trials, and the Central Limit Theorem with practical examples and visualizations.
On the Unreasonable Effectiveness of Compressive Imaging - Ben Adcock, Simon Fraser University
Explores compressive imaging's effectiveness, discussing compressed sensing theory, optimal sampling strategies, and practical applications in data science and imaging technology.
Affine Spline Insights into Deep Learning - Richard Baraniuk, Rice University
Explore deep learning through affine splines, understanding neural networks as matched filterbanks and partition-based signal distances. Gain insights into data augmentation and future research directions.
Deep Dictionary Learning Approaches for Image Super-Resolution - Pier Luigi Dragotti, Imperial
Explore cutting-edge deep dictionary learning techniques for image super-resolution with Pier Luigi Dragotti from Imperial College, bridging mathematics and data science in this advanced workshop.
Cookbook Lower Bounds for Statistical Inference in Distributed and Constrained Settings - Part 3
Explore advanced statistical inference techniques for distributed and constrained environments, focusing on lower bound analysis and practical applications in complex data scenarios.
Testing Dependency of Databases - Lecture
Explore statistical methods for detecting dependencies between random databases, including hypothesis testing and phase transitions in error probability based on database dimensions and distributions.
The Role of Convexity in Data-Driven Decision-Making
Explore data-driven decision-making through distributionally robust optimization, focusing on computation, statistics, and real-time implementation aspects. Gain insights into online optimization applications.
Inverse Problems on Graphs with Geometric Deep Learning
Explores geometric deep learning for graph inverse problems, focusing on non-Euclidean geometries, physics-based approaches, and applications in particle physics and community detection.
New Challenges in Computational Statistics
Explore cutting-edge computational statistics challenges and innovations in this lecture by Professor Gareth Roberts, focusing on complex data analysis and algorithm advancements in statistical science.
Langevin Monte Carlo: Randomized Midpoint Method Revisited
Explore advanced Langevin Monte Carlo techniques, focusing on the Randomized Midpoint method's superior sampling error guarantees and recent improvements for high-dimensional target distributions.
Integrating Mechanistic Models with Computational Statistics and Machine Learning - New Insights in Biology
Explore integrating mechanistic models with computational statistics and machine learning for new insights in cell and developmental biology. Tackle challenges in analyzing complex quantitative data.
Automated Scalable Bayesian Inference via Data Summarization - 2018
Explore scalable Bayesian inference techniques using data summarization. Learn about coresets, theoretical guarantees, and practical applications in large-scale data settings.
Functional Priors for Bayesian Deep Learning
Explore functional priors for Bayesian deep learning, focusing on imposing Gaussian process priors on neural networks through Wasserstein distance minimization.
Independent Finite Approximations for Bayesian Nonparametric Inference
Explore independent finite approximations for Bayesian nonparametric inference, comparing them to truncated finite approximations and analyzing their performance in real-world applications.
High-dimensional Statistics and Approximate Message Passing Algorithms - Course 3
Explore high-dimensional statistics and approximate message passing algorithms in this advanced mathematics lecture. Gain insights into cutting-edge research in discrete probability and statistics.
High-dimensional Statistics and Approximate Message Passing Algorithms - Course 3
Explore high-dimensional statistics and approximate message passing algorithms with Cynthia Rush. Gain insights into advanced mathematical concepts and their applications in probability and statistics.
High-dimensional Statistics and Approximate Message Passing Algorithms - Course 3
Explore high-dimensional statistics and approximate message passing algorithms in this advanced mathematics lecture. Gain insights into cutting-edge research in discrete probability and statistics.
Two Decentralised Learning Problems - Sketching and Policy Evaluation - Justin Romberg, Georgia Tech
Explore cutting-edge decentralized learning techniques in sketching and policy evaluation with Justin Romberg from Georgia Tech. Gain insights into advanced mathematics for data science.
Bayesian Inference Using Probabilistic Programming
Explore Bayesian inference techniques and probabilistic programming concepts with expert insights from Hong Ge at LAFI'24.