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Alexandr Katrutsa | Andrzej Cichocki | Ivan Oseledets | Talgat Daulbaev | Julia Gusak | A. Cichocki | Talgat Daulbaev | A. Katrutsa | Julia Gusak | I. Oseledets
[1] Timothy M. Hospedales,et al. Meta-Learning in Neural Networks: A Survey , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Matematik,et al. Numerical Methods for Ordinary Differential Equations: Butcher/Numerical Methods , 2005 .
[3] Michael W. Mahoney,et al. Continuous-in-Depth Neural Networks , 2020, ArXiv.
[4] Philipp Hennig,et al. When are Neural ODE Solutions Proper ODEs? , 2020, ArXiv.
[5] Matthew J. Johnson,et al. Learning Differential Equations that are Easy to Solve , 2020, NeurIPS.
[6] Eric Z. Chen,et al. MRI Image Reconstruction via Learning Optimization Using Neural ODEs , 2020, MICCAI.
[7] Philip H. S. Torr,et al. STEER : Simple Temporal Regularization For Neural ODEs , 2020, ArXiv.
[8] J. Duncan,et al. Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE , 2020, ICML.
[9] Lars Ruthotto,et al. Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows , 2020, ArXiv.
[10] Alexandr Katrutsa,et al. Towards Understanding Normalization in Neural ODEs , 2020, ICLR 2020.
[11] Asha Anoosheh,et al. Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs , 2020, ArXiv.
[12] Alexandr Katrutsa,et al. Interpolated Adjoint Method for Neural ODEs , 2020, ArXiv.
[13] D. Vetrov,et al. Stochasticity in Neural ODEs: An Empirical Study , 2020, ICLR 2020.
[14] J. Zico Kolter,et al. Fast is better than free: Revisiting adversarial training , 2020, ICLR.
[15] David Duvenaud,et al. Scalable Gradients for Stochastic Differential Equations , 2020, AISTATS.
[16] Jiashi Feng,et al. On Robustness of Neural Ordinary Differential Equations , 2019, ICLR.
[17] Roberto Caldelli,et al. On the Robustness to Adversarial Examples of Neural ODE Image Classifiers , 2019, 2019 IEEE International Workshop on Information Forensics and Security (WIFS).
[18] Ming-Yu Liu,et al. PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Cho-Jui Hsieh,et al. Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise , 2019, ArXiv.
[20] Jason Yosinski,et al. Hamiltonian Neural Networks , 2019, NeurIPS.
[21] Peisong Wang,et al. ODE-Inspired Network Design for Single Image Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Edward De Brouwer,et al. GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series , 2019, NeurIPS.
[23] Maxim Raginsky,et al. Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit , 2019, ArXiv.
[24] Yee Whye Teh,et al. Augmented Neural ODEs , 2019, NeurIPS.
[25] Kurt Keutzer,et al. ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs , 2019, IJCAI.
[26] David Duvenaud,et al. FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models , 2018, ICLR.
[27] Eldad Haber,et al. Deep Neural Networks Motivated by Partial Differential Equations , 2018, Journal of Mathematical Imaging and Vision.
[28] David Duvenaud,et al. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series , 2019, NeurIPS.
[29] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[30] G. Karniadakis,et al. Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems , 2018, 1801.01236.
[31] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[32] Frederick Tung,et al. Multi-level Residual Networks from Dynamical Systems View , 2017, ICLR.
[33] Bin Dong,et al. Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations , 2017, ICML.
[34] Raquel Urtasun,et al. The Reversible Residual Network: Backpropagation Without Storing Activations , 2017, NIPS.
[35] Dahua Lin,et al. PolyNet: A Pursuit of Structural Diversity in Very Deep Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Gregory Shakhnarovich,et al. FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.
[37] Eric A Sobie,et al. An Introduction to Dynamical Systems , 2011, Science Signaling.
[38] E. Hairer,et al. Solving Ordinary Differential Equations II , 2010 .
[39] J. Lambert. Numerical Methods for Ordinary Differential Equations , 1991 .
[40] J. Dormand,et al. A family of embedded Runge-Kutta formulae , 1980 .