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We propose a new approach to model the collective dynamics of a population of particles evolving with time.
Computational optimal transport
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Sinkhorn Distances: Lightspeed Computation of Optimal Transport
M. Cuturi · 2013
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On the difficulty of training Recurrent Neural Networks
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Entropic Approximation of Wasserstein Gradient Flows
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Variational Inference with Normalizing Flows
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Optimal Transport for Applied Mathematicians
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Stochastic partial differential equation based modelling of large space–time data sets
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Learning Population-Level Diffusions with Generative Recurrent Networks
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Deep Residual Learning for Image Recognition
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Input Convex Neural Networks
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Sample Complexity of Sinkhorn Divergences
A. Genevay, L. Chizat, F. Bach, M. Cuturi, and G. Peyré · 2019
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FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
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Estimating ensemble flows on a hidden Markov chain
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Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
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Current best practices in single-cell RNA-seqanalysis: a tutorial
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Visualizing structure and transitions in high-dimensional biological data
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H. Edwards and A. Storkey · 2017
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Structured Inference Networks for Nonlinear State Space Models
R. Krishnan, U. Shalit, and D. Sontag · 2017
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Unrolled Generative Adversarial Networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2017
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On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
A. Ramdas, N. G. Trillos, and M. Cuturi · 2017
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{ \{ Euclidean, metric, and Wasserstein } \} gradient flows: an overview
F. Santambrogio · 2017
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Attention is All you Need
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Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
G. Schiebinger, J. Shu, M. Tabaka, B. Cleary, V. Subramanian, A. Solomon, J. Gould, S. Liu, S. Lin, P. Berube, et al · 2019
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Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
K. D. Yang and C. Uhler · 2019
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Scalable Gradients and Variational Inference for Stochastic Differential Equations
X. Li, T.-K. L. Wong, R. T. Chen, and D. K. Duvenaud · 2020
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Optimizing Millions of Hyperparameters by Implicit Differentiation
J. Lorraine, P. Vicol, and D. Duvenaud · 2020
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Multiple object tracking: A literature review
W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T.-K. Kim · 2020
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Optimal transport mapping via input convex neural networks
A. Makkuva, A. Taghvaei, S. Oh, and J. Lee · 2020
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Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
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Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics
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Predicting cell lineages using autoencoders and optimal transport
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Primal Dual Methods for Wasserstein Gradient Flows
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Pot: Python optimal transport
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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
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Large-Scale Wasserstein Gradient Flows
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Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
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