GFlowNet Foundations and Applications in Biological Sequence Design
Explore GFlowNet foundations and applications in biological sequence design. Learn about flow conservation, transition probabilities, and practical implementations for molecule generation and peptide design.
Towards Good Validation Metrics for Generative Models in Offline Model-Based Optimization
Explore principled evaluation frameworks for generative models in offline model-based optimization. Learn about validation metrics, dataset challenges, and strategies for measuring extrapolation capabilities.
DiGress: Discrete Denoising Diffusion for Graph Generation
Explore DiGress, a discrete denoising diffusion model for graph generation with categorical attributes. Learn about its innovative approach, challenges, and state-of-the-art performance in molecular and non-molecular datasets.
Manifold Diffusion Fields - Learning Generative Models of Continuous Functions on Riemannian Manifolds
Explore Manifold Diffusion Fields for generating continuous functions on Riemannian manifolds. Learn about spectral geometry, intrinsic coordinate systems, and applications in AI for drug discovery.
Multiflow: Protein Structure and Sequence Co-generation
Explore protein structure and sequence co-generation using Discrete Flow Models and Multiflow, a novel approach combining discrete and continuous data for improved AI-driven drug discovery.
Protein Discovery with Discrete Walk-Jump Sampling
Explore discrete generative modeling for protein discovery using Walk-Jump Sampling. Learn to train energy functions, sample with Langevin MCMC, and optimize for distributional conformity in antibody design.
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.
GenPhys: From Physical Processes to Generative Models
Explore how physical processes inspire new generative models, including diffusion and Poisson flow. Learn to translate PDEs into AI algorithms and expand the design space of generative approaches.
Machine Learning for Molecular Structures and Drug Discovery
Explore machine learning applications in molecular structures, generative models, and protein analysis. Learn about tools like LigDream, TorchMD, and PlayMolecules for drug discovery and chemistry research.
The Statistical Structure of Identifiable Generative Models
Explore the statistical structure of identifiable generative models, focusing on causal representation learning, model identifiability, and unique inferences from observations under suitable assumptions.
Equivariant Flow Matching for Deep Generative Models in Physics
Explore equivariant flow matching for efficient, simulation-free training of equivariant CNFs in physics and drug discovery, improving sampling efficiency and scalability for many-particle systems.
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Explore PFGM++, a physics-inspired generative model unifying diffusion models and PFGMs. Learn about its flexible embedding, unbiased objectives, and superior performance on image datasets.
Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Explore advanced techniques for improving continuous normalizing flows, including generalized conditional flow matching and optimal transport CFM, enhancing generative modeling for various applications.
Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models
Explore particle guidance for diverse sampling in diffusion models, enhancing diversity and efficiency in generative tasks like image and molecular conformer generation.
Designing Losses for Data-Free Training of Normalizing Flows on Boltzmann Distributions
Explore advanced techniques for training normalizing flows on Boltzmann distributions without data, addressing mode collapse and optimizing high-dimensional distributions in molecular configuration generation.
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.
Joint Energy-based Models for Speech
Explore generative models, focusing on Energy-based Models and Joint Energy-based Models for speech, with potential applications in multiple tasks.
Deep Learning of Generative Models - 2014
Explore generative models in deep learning with Yoshua Bengio, covering motivations, applications, and fundamental challenges in AI and machine learning.
Synthetic Data in Medical Imaging: Are We There Yet?
Explore synthetic data's potential and challenges in medical imaging, focusing on recent advancements and future prospects for improved healthcare applications.
Neural Network Renormalization Group
Explore multi-scale entanglement renormalization and neural network renormalization group, connecting deep learning with quantum physics concepts for advanced computational techniques.
Learning Quantum with Generative Models
Explore generative models for quantum state learning, covering neural networks, RBMs, and applications to GHZ states and local Hamiltonians.