Lecture

Diffusion Models

Related lectures (31)

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Score-based Generative Modelling

Explores score-based generative modelling through stochastic differential equations, emphasizing denoising score matching and diffusion probabilistic models.

Concentration Inequalities

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Distribution Estimation

Covers the estimation of distributions using samples and probability models.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Orchestration Graph Concepts

Explains the use of operators for transferring data between activities in an orchestration graph.

Untitled

Sparsest Cut: Bourgain's Theorem

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Property Testing

Covers the concept of property testing using statistical methods.

Distribution Estimation

Covers the estimation of distributions using various methods such as minimum loss and expectation.

Finite Element Method: Weak Solutions

Covers weak solutions in the finite element method, emphasizing continuity and the Cauchy-Schwarz inequality.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.

Operators: Data Transfer and Manipulation

Covers the concept of operators for data transfer and manipulation between activities, including aggregation, distribution, and social operators.

Understanding Statistics & Experimental Design

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.