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Ruthotto, Lars

  1. Deep Neural Networks Motivated by Partial Differential Equations
    2018/04/12 by Ruthotto, Lars, Haber, Eldad · 22 citations
    #65K10 #68T45 #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  2. An Introduction to Deep Generative Modeling
    2021/03/09 by Ruthotto, Lars, Haber, Eldad · 7 citations
    #68T07 #FOS: Computer and information sciences #Machine Learning (cs.LG)
  3. Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
    2023/03/08 by Paul Hagemann, Hagemann, Paul, Mildenberger, Sophie +6 · 6 citations
    Computer Science · Physics and Astronomy · #60H10 #65D18 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Probability (math.PR)
  4. A Neural Network Approach for Stochastic Optimal Control
    2022/09/27 by Li, Xingjian, Verma, Deepanshu, Ruthotto, Lars · 6 citations
    #FOS: Mathematics #Optimization and Control (math.OC)
  5. Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
    2020/05/27 by Derek Onken, Onken, Derek, Lars Ruthotto +1 · 4 citations
    Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Time Series Analysis and Forecasting
  6. Reversible Architectures for Arbitrarily Deep Residual Neural Networks
    2017/09/12 by Bo Chang, Chang, Bo, Lili Meng +9 · 4 citations
    Computer Science · Physics and Astronomy · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Model Reduction and Neural Networks
  7. Multivariate Quantile Function Forecaster
    2022/02/23 by Kan, Kelvin, Aubet, François-Xavier, Januschowski, Tim +4 · 3 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  8. jInv -- a flexible Julia package for PDE parameter estimation
    2016/06/23 by Ruthotto, Lars, Treister, Eran, Haber, Eldad · 2 citations
    #FOS: Computer and information sciences #Mathematical Software (cs.MS)
  9. Neural Network Approaches for Parameterized Optimal Control
    2024/02/15 by Verma, Deepanshu, Winovich, Nick, Ruthotto, Lars +1 · 4 citations
    #FOS: Mathematics #Optimization and Control (math.OC)
  10. Train Like a (Var)Pro: Efficient Training of Neural Networks with\n Variable Projection
    2020/07/26 by Elizabeth Newman, Lars Ruthotto, Newman, Elizabeth +5 · 2 citations
    Computer Science · Physics and Astronomy · #49M15 #68T05 #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
  11. Differential Equations for Continuous-Time Deep Learning
    2024/01/08 by Lars Ruthotto, Ruthotto, Lars · 3 citations
    Physics and Astronomy · #Model Reduction and Neural Networks
  12. An Uncertainty-Weighted Asynchronous ADMM Method for Parallel PDE Parameter Estimation
    2018/06/01 by Fung, Samy Wu, Ruthotto, Lars · 1 citation
    #FOS: Mathematics #Numerical Analysis (math.NA)
  13. Gauss-Newton Optimization for Phase Recovery from the Bispectrum
    2018/12/12 by Herring, James L., Nagy, James, Ruthotto, Lars · 1 citation
    #FOS: Mathematics #Numerical Analysis (math.NA)
  14. IMEXnet: A Forward Stable Deep Neural Network
    2019/03/06 by Haber, Eldad, Lensink, Keegan, Treister, Eran +1 · 1 citation
    #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
  15. LSEMINK: A Modified Newton-Krylov Method for Log-Sum-Exp Minimization
    2023/07/10 by Kan, Kelvin, Nagy, James G., Ruthotto, Lars · 2 citations
    #FOS: Mathematics #Optimization and Control (math.OC)
  16. slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
    2021/09/28 by Newman, Elizabeth, Chung, Julianne, Chung, Matthias +1 · 1 citation
    #65C20 #65K99 #68T07 #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA)
  17. Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
    2023/10/25 by Wang, Zheyu Oliver, Baptista, Ricardo, Marzouk, Youssef +2 · 1 citation
    #62F15 #62M45 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  18. Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
    2020/12/11 by Kelvin K.W. Kan, James G. Nagy, Kan, Kelvin +3 · 1 citation
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques