Eric Vanden‐Eijnden

1. Building Normalizing Flows with Stochastic Interpolants
   2022/09/30 by Michael S. Albergo, Albergo, Michael S., Eric Vanden‐Eijnden +2 · 1 voice · 193 citations
   Computer Science · Medicine · Physics and Astronomy · #Advanced Neuroimaging Techniques and Applications #Generative Adversarial Networks and Image Synthesis #Model Reduction and Neural Networks #cs.LG #stat.ML
2. Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
   2023/03/15 by Michael S. Albergo, Albergo, Michael S., Nicholas M. Boffi +3 · 183 citations
   Computer Science · Economics, Econometrics and Finance · Mathematics · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Probability (math.PR) #Statistical Methods and Bayesian Inference #Stochastic processes and financial applications
3. SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
   2024/01/16 by Nanye Ma, Ma, Nanye, Michael S. Albergo +8 · 159 citations
   Computer Science · Physics and Astronomy · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Music and Audio Processing
4. Transition-Path Theory and Path-Finding Algorithms for the Study of Rare Events
   2008/11/11 by E Weinan, Weinan E, Eric Vanden‐Eijnden +1 · 32 citations
   Physics and Astronomy · #Cold Atom Physics and Bose-Einstein Condensates #Quantum, superfluid, helium dynamics #Theoretical and Computational Physics
5. Towards a Theory of Transition Paths
   2006/05/01 by Weinan E., E Weinan, Eric Vanden-Eijnden +1 · 17 citations
   Physics and Astronomy · Biochemistry, Genetics and Molecular Biology · Decision Sciences · #stochastic dynamics and bifurcation #Diffusion and Search Dynamics #Probabilistic and Robust Engineering Design
6. Stochastic interpolants with data-dependent couplings
   2023/10/05 by Michael S. Albergo, Albergo, Michael S., Nicholas M. Boffi +6 · 25 citations
   Computer Science · Biochemistry, Genetics and Molecular Biology · #Generative Adversarial Networks and Image Synthesis #Cell Image Analysis Techniques #Computer Graphics and Visualization Techniques
7. Flow map matching with stochastic interpolants: A mathematical framework for consistency models
   2024/06/11 by Nicholas M. Boffi, Boffi, Nicholas M., Michael S. Albergo +3 · 22 citations
   Engineering · Computer Science · #Traffic Prediction and Management Techniques #Time Series Analysis and Forecasting
8. How to build a consistency model: Learning flow maps via self-distillation
   2025/05/24 by Nicholas M. Boffi, Boffi, Nicholas M., Michael S. Albergo +4 · 2 voices · 26 citations
   Computer Science · #Intelligent Tutoring Systems and Adaptive Learning #Online Learning and Analytics #cs.CV #cs.LG
9. On the Accuracy of Finite-Volume Schemes for Fluctuating Hydrodynamics
   2009/06/12 by A. Donev, Donev, A., Eric Vanden‐Eijnden +5 · 3 citations
   Earth and Planetary Sciences · Engineering · #Computational Fluid Dynamics and Aerodynamics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Meteorological Phenomena and Simulations #Soft Condensed Matter (cond-mat.soft) #Statistical Mechanics (cond-mat.stat-mech)
10. Learning sparse features can lead to overfitting in neural networks
    2022/06/24 by Leonardo Petrini, Petrini, Leonardo, Francesco Cagnetta +5 · 3 citations
    Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Statistical Methods and Inference
11. Multimarginal generative modeling with stochastic interpolants
    2023/10/05 by Michael S. Albergo, Albergo, Michael S., Nicholas M. Boffi +5 · 4 citations
    Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Generative Adversarial Networks and Image Synthesis
12. Deep learning probability flows and entropy production rates in active matter
    2024/06/11 by Nicholas M. Boffi, Eric Vanden‐Eijnden · 1 voice · 3 citations
    Neuroscience · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Micro and Nano Robotics #Neural dynamics and brain function
13. Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
    2024/04/01 by Huan Zhang, Yifan Chen, Zhang, Huan +5 · 3 citations
    Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Advanced Measurement and Metrology Techniques
14. On Energy-Based Models with Overparametrized Shallow Neural Networks
    2021/04/15 by Carles Domingo-Enrich, Alberto Bietti, Domingo-Enrich, Carles +5 · 1 citation
    Physics and Astronomy · Computer Science · Materials Science · #Model Reduction and Neural Networks #Generative Adversarial Networks and Image Synthesis #Machine Learning in Materials Science
15. FEAT: Free energy Estimators with Adaptive Transport
    2025/04/15 by Jiajun He, Yuanqi Du, He, Jiajun +12 · 1 voice · 4 citations
    Materials Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Machine Learning in Materials Science #Quantum many-body systems #cs.LG #physics.chem-ph #physics.comp-ph #stat.ML
16. Learning to Sample Better
    2023/10/17 by Michael S. Albergo, Albergo, Michael S., Eric Vanden‐Eijnden +1 · 1 citation
    Computer Science · #Cellular Automata and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
17. Unveiling the Phase Diagram and Reaction Paths of the Active Model B with the Deep Minimum Action Method
    2023/09/26 by Ruben Zakine, Zakine, Ruben, Eric Simonnet +3 · 1 citation
    Materials Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #FOS: Physical sciences #Machine Learning in Materials Science #Soft Condensed Matter (cond-mat.soft) #Statistical Mechanics (cond-mat.stat-mech) #Theoretical and Computational Physics
18. Lipschitz-Guided Design of Interpolation Schedules in Generative Models
    2025/09/01 by Yifan Chen, Eric Vanden‐Eijnden, Chen, Yifan +3 · 3 citations
    Physics and Astronomy · Computer Science · #Model Reduction and Neural Networks #Generative Adversarial Networks and Image Synthesis #Stochastic Gradient Optimization Techniques
19. Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization
    2020/08/11 by Grant M. Rotskoff, Rotskoff, Grant M., Andrew Mitchell +3 · 1 citation
    Computer Science · Engineering · #Machine Learning and Algorithms #Fault Detection and Control Systems #Adversarial Robustness in Machine Learning
20. Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking
    2024/11/21 by Nicholas M. Boffi, Boffi, Nicholas M., Eric Vanden‐Eijnden +1 · 1 citation
    Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Probability (math.PR) #Quantum many-body systems #Statistical Mechanics (cond-mat.stat-mech) #stochastic dynamics and bifurcation