Score Matching via Differentiable Physics
Explore score matching via differentiable physics for inverse problems. Learn about combining physics operators with stochastic differential equations to generate plausible trajectories towards given end states.
Flow Annealed Importance Sampling Bootstrap - Machine Learning for Complex Distributions
Explore Flow Annealed Importance Sampling Bootstrap for approximating complex target distributions. Learn about normalizing flows, annealed importance sampling, and applications to molecular systems.
Artificial Chemical Intelligence: AI for Chemistry and Chemistry for AI
Explore the integration of AI with chemistry, focusing on trustworthy frameworks for molecular discovery. Learn about innovative approaches to predict mechanisms and overcome challenges in computational chemistry.
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.
Diffusion Models for Sampling Rare Events in Chemistry
Explore advanced diffusion models for generating transition state structures in chemical reactions, accelerating reaction mechanism discovery and network exploration.
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.
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.
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.
Clifford Group Equivariant Neural Networks
Explore Clifford Group Equivariant Neural Networks for E(n)-equivariant network construction. Discover theoretical foundations, methodology, and applications in various dimensions and tasks.
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.
An Automata-Based Framework for Verification and Bug Hunting in Quantum Circuits
Explore a novel automata-based approach for analyzing quantum circuits, enabling efficient verification and bug detection in large-scale quantum systems using tree automata and algebraic representations.
Quantum Bisimilarity via Barbs and Contexts: Curbing the Power of Non-Deterministic Observers
Explore quantum bisimilarity in process calculi, focusing on a new Linear Quantum CCS approach that aligns with quantum theory principles and refines observational semantics for quantum systems.
Information Exchange in Distributed Blockchain Systems - Epidemic Model Approach
Explore architectural drivers, communication models, and distributed consensus in complex blockchain systems. Learn from real-world experiences in designing and implementing large-scale node networks.
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.
Auditory Attention: From Saliency to Models to Applications
Explore auditory attention models and applications, from saliency to speech recognition, understanding human-machine interactions through implicit signals like eye gaze and face pose.
Deterministic Annealing for Clustering, Classification and Speech Recognition
Explore deterministic annealing for improved clustering and classification, avoiding local optima and optimizing various structures in machine learning applications.
Experimental Investigation of a Microring-Based Time-Delay Reservoir Computing
Explore microring-based time-delay reservoir computing through experimental investigation, delving into advanced concepts and practical applications in photonic computing.
Stochastic Optical Quantum Circuit Simulator (SOQCS) - Model and Inner Workings
Explore el modelo y funcionamiento interno del Simulador de Circuitos Cuánticos Ópticos Estocásticos (SOQCS) para avanzar en la computación cuántica óptica.