Lars Ruthotto

1. Stable architectures for deep neural networks
   2017/11/14 by Eldad Haber, Lars Ruthotto · 39 citations
   Computer Science · Physics and Astronomy · #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Stochastic Gradient Optimization Techniques
2. Deep Neural Networks Motivated by Partial Differential Equations
   2018/04/12 by Lars Ruthotto, Eldad Haber, Ruthotto, Lars +1 · 23 citations
   #65K10 #68T45 #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
3. Reversible Architectures for Arbitrarily Deep Residual Neural Networks
   2017/09/12 by Bo Chang, Lili Meng, Chang, Bo +9 · 11 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
4. Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
   2020/05/27 by Derek Onken, Onken, Derek, Lars Ruthotto +1 · 6 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
5. Layer-Parallel Training of Deep Residual Neural Networks
   2018/12/11 by Stefanie Günther, Günther, S., Lars Ruthotto +7 · 4 citations
   Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Advanced Neural Network Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Seismic Imaging and Inversion Techniques
6. 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)
7. 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
8. 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
9. LSEMINK: A Modified Newton-Krylov Method for Log-Sum-Exp Minimization
   2023/07/10 by Kelvin K.W. Kan, James G. Nagy, Kan, Kelvin +3 · 2 citations
   Computer Science · Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Mathematics #Matrix Theory and Algorithms #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques
10. An Uncertainty-Weighted Asynchronous ADMM Method for Parallel PDE\n Parameter Estimation
    2018/06/01 by Samy Wu Fung, Lars Ruthotto, Fung, Samy Wu +1 · 1 citation
    Computer Science · Engineering · #FOS: Mathematics #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Target Tracking and Data Fusion in Sensor Networks
11. Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
    2020/12/11 by Kelvin K.W. Kan, Kan, Kelvin, James G. Nagy +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