MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Explore MultiDiffusion: a framework for controllable image generation using pre-trained diffusion models. Learn about panorama creation, high-resolution outputs, and versatile user controls without retraining.
Additive Decoders for Latent Variables Identification
Explore additive decoders for latent variable identification and out-of-support image generation in representation learning. Gain insights into nonlinear ICA and object-centric representation learning.
Generalization in Diffusion Models from Geometry-Adaptive Harmonic Representation
Explore generalization in diffusion models through geometry-adaptive harmonic representation. Analyze denoising DNNs, score functions, and inductive biases to understand high-dimensional density learning and optimal basis adaptation.
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.
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Explore advanced diffusion models with Consistency Trajectory Models, improving sample quality and efficiency in AI-driven image generation. Learn novel sampling techniques and state-of-the-art results.
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.
How AI Can Actually Help Your Software Development Teams
Explore AI's practical applications across the software development lifecycle, addressing cultural and technical challenges for effective team integration.
Responsible AI and Ethics in AI Engineering
Explore ethical AI engineering practices and their societal impact. Learn to adapt to the changing landscape and consider human factors when building AI-powered systems and products.
Geometric Machine Learning for Molecular Medicine
Explore geometric machine learning applications in molecular medicine, focusing on AI models for therapeutic science and drug repurposing using knowledge graphs and contextual approaches.
The Future of AI for Drug Discovery
Explore the future of AI in drug discovery with leading experts discussing challenges, opportunities, and practical applications of machine learning in molecular research.
Accelerating Cryptic Pocket Discovery with Deep Learning
Explore deep learning techniques for accelerating cryptic pocket discovery in protein structures, focusing on simulation, key degrees of freedom, and tools like PocketMiner and AlphaFold.
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.
Pre-training via Denoising for Molecular Property Prediction
Explore pre-training via denoising for molecular property prediction, learning force fields from equilibrium structures to improve generalization and achieve state-of-the-art results on QM9 dataset.
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.
AI and the Future of Software Engineering
Explore AI's impact on software engineering, examining productivity gains, ethical concerns, and necessary shifts in team structures and skills for the future of development.
Expedition to the LLM and AI Frontier - Navigating the Modern AI Landscape
Navigate the LLM and AI landscape as a data science leader. Learn to champion your team, educate company leaders, and leverage AI tools for successful implementation in your organization.
Towards Foundation Models for Knowledge Graph Reasoning
Explore ULTRA, a novel approach for universal and transferable graph representations in knowledge graph reasoning, enabling zero-shot inductive inference and fine-tuning on unseen graphs.
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.