Lecture

Deep Generative Models in Drug Discovery

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Related lectures (64)

Mathematics of Data: Models and Learning

Explores models, learning paradigms, and applications in Mathematics of Data.

Optimisation in Energy Systems

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Score-Based Generative Models

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

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Boolean Algebra: Properties and Optimization

Explores Boolean algebra properties and optimization techniques using Karnaugh diagrams and De Morgan's theorems.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Scaling Language Models: Efficiency and Deployment

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Deep and Robust Reinforcement Learning Techniques

Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Solving Parity Games in Practice

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Deep Generative Models: Variational Autoencoders & GANs

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Optimization: Classical Problems

Covers classical optimization problems, brute force algorithms, and integer linear optimization.

Stochastic Softmax Tricks

Explores stochastic softmax tricks, reparametrization, and argmax, addressing challenges in expectation estimation and gradient variance.

PCA: Derivation and Optimization

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Stochastic Gradient Descent

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.

Infectious Diseases: Small Molecule Drugs

Explores small molecule drugs for infectious diseases, covering global mortality rates, viral and bacterial injuries, antiviral and antibacterial agents, and the development of COX2 inhibitors.

Computational Molecular Design

Explores Computational Molecular Design, focusing on Mathematical Theory, High Performance Computing, and In Vivo Experiments, with an emphasis on quantum chemistry and electron dynamics.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.