Lectures related to Generative Neural Networks: Sampling and Training

Diffusion Models in Generative Modeling

Explores diffusion models in generative modeling, covering probability estimation, data generation, and model evaluation.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Natural Language Generation: Decoding Techniques and Training Challenges

Covers decoding methods and training challenges in natural language generation.

Wireless Receivers: Time and Phase Offset

Covers the impact and compensation of time and phase offset in wireless receivers.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Score-Based Generative Models

Delves into score-based generative models, exploring learning natural distributions and the impact of neural network architecture on robustness.

Markov Chain Monte Carlo: Detailed Balance & Neural Networks

Covers Markov chain Monte Carlo and neural networks' role in quantum states representation and ground state approximation for frustrated spins systems.

Convolutional Neural Networks: Filters and Channels

Explores weight sharing, filters, channels, and data augmentation in convolutional neural networks.

Sampling a Probability Distribution

Explores sampling a probability distribution and structure functions in statistical analysis.

Hardware Technologies and Applications

Explores hardware technologies, quantum electronics, cryogenic systems, quantum computing, and ADC architectures.

Convolutional Networks: Motivation & Ideas

Explores the motivation and ideas behind convolutional networks, emphasizing weight sharing and pooling layers.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Diffusion Models

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Max Entropy and Monte Carlo

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Monte Carlo Techniques: Sampling and Simulation

Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.

MCMC with Metropolis

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.

Data Representations and Processing in Machine Learning

Covers data representations and processing techniques essential for effective machine learning algorithms.