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.
An Optimal Control Perspective on Diffusion-Based Generative Modeling
Explore optimal control theory's connection to diffusion-based generative modeling. Gain insights into stochastic differential equations, Hamilton-Jacobi-Bellman equations, and novel sampling methods for unnormalized densities.
Transport, Variational Inference and Diffusions
Explore connections between optimal transport and variational inference, focusing on stochastic differential equations and Girsanov transformations. Discover novel sampling and generative modeling techniques.
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.
Action Matching: Learning Stochastic Dynamics from Samples
Explore Action Matching for learning stochastic dynamics from samples. Discover applications in quantum systems, biology, and generative modeling. Gain insights into novel approaches for simulating trajectories.
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.
Generative Language Models in Molecular Discovery: Regression Transformer, GT4SD and Beyond
Explore generative language models for molecular discovery, focusing on the Regression Transformer and GT4SD toolkit. Learn about multitask approaches bridging regression and conditional generation for scientific 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.
Mirror Diffusion Models for Constrained and Watermarked Generation
Explore Mirror Diffusion Models for generating data on constrained sets, enhancing watermarking capabilities, and improving AI safety in drug discovery applications.
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.
Vector Search at Uber - Implementing and Scaling KNN Search with Apache Lucene
Explore Uber's implementation of Vector Search using Apache Lucene, enabling semantic search and Gen AI support. Learn about HNSW algorithm integration, live vector updates, and scalability optimizations.
Generative AI: Concepts, Tools, and Applications for Data Professionals
Explore generative AI's workings, limitations, tools, and applications to enhance data projects and estates. Learn practical insights for data professionals.
AI-Enabled Chatbot to Production MLOps Application - Red Hat AI Portfolio Demo
Explore Red Hat's AI portfolio: from simple chatbots to full-scale MLOps applications for enterprise. Learn about Podman AI Lab, RHEL AI, and OpenShift AI for building and deploying AI solutions.
Unlocking the Power of AI: Accessible Language Model Tuning with InstructLab
Explore integrating Generative AI into applications using InstructLab for accessible LLM tuning and Podman AI Lab for model serving and development.
Generative AI for Application Modernization with Konveyor AI - Demo and Introduction
Explore Konveyor AI's integration of generative AI and static code analysis for efficient Java EE to Quarkus migration, with a practical demo using the "coolstore" application.
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.
Intersection of Physics and AI - Incorporating Physics into Data-Driven Computer Vision
Explore the fusion of physics and AI, covering applications in NLP, healthcare, and computer vision. Learn innovative approaches to incorporate physics into data-driven models.
Generative AI for Creative Design of Novel Materials
Explore generative AI techniques for innovative material design, focusing on creative approaches to develop novel materials with desired properties and functionalities.