Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Explore neural set function extensions for integrating discrete functions into neural networks, focusing on continuous domain extensions and high-dimensional representations for combinatorial optimization.
Converging Advances to Accelerate Molecular Simulation
Explore advances in molecular simulation through deep learning, ASIC architectures, and quantum computation. Discover potential applications in sustainable technologies, drug development, and material design.
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.
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.
Unsupervised Learning of Group Invariant and Equivariant Representations
Explore unsupervised learning of group invariant and equivariant representations. Gain insights into symmetry in data, practical examples, and results for graphs and various symmetries.
Local Search GFlowNets - Enhancing Sampling Quality for Drug Discovery
Explore Local Search GFlowNets, a novel algorithm enhancing sampling quality for structured sequence generation in drug discovery and other fields through improved targeting of high-reward regions.
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.
Linear Structure of High-Level Concepts in Text-Controlled Generative Models
Explore the linear structure of high-level concepts in text-controlled generative models, focusing on embedding, representation, and semantic encoding for precise model control.
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.
A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks
Explore advanced techniques for building efficient 3D equivariant graph neural networks, focusing on local substructure encoding and frame transition encoding for improved expressiveness and performance.
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.
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.
A Computational Framework for Solving Wasserstein Lagrangian Flows
Explore a computational framework for solving Wasserstein Lagrangian flows, covering optimal transport variations and applications in single-cell trajectory inference.
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.
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.
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.
Lilac: A Modal Separation Logic for Conditional Probability
Explore Lilac, a novel separation logic for probabilistic programs, introducing a modality for conditional probability reasoning and validating its effectiveness through formal verification examples.
Type-Checking CRDT Convergence
Explore Propel, a language with a type system for verifying algebraic properties in CRDT implementations. Learn about its case analysis and induction approach for ensuring correctness.
Verification of a Subgraph Construction Algorithm for Geometric Graphs - VMCAI 2024
Explore automated verification of a subgraph construction algorithm, focusing on geometric graph properties, edge removal, and connectivity preservation in Euclidean plane representations.