Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent
Observation Framework

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework

24 July 2023

William Andersson

Josef Teichmann

Papers citing "Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework"

10 / 10 papers shown

Title
How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer Jakob Heiss Josef Teichmann Hanna Wutte AI4CE 54 2 0 20 Mar 2023
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization Jakob Heiss Josef Teichmann Hanna Wutte MLT 37 2 0 31 Dec 2021
Variational Marginal Particle Filters Jinlin Lai Justin Domke Daniel Sheldon 54 9 0 30 Sep 2021
What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory Rahul Parhi Robert D. Nowak MLT 60 71 0 07 May 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos James Thornton George Deligiannidis Arnaud Doucet OT 63 68 0 15 Feb 2021
Neural Rough Differential Equations for Long Time Series James Morrill C. Salvi Patrick Kidger James Foster Terry Lyons AI4TS 66 132 0 17 Sep 2020
Neural Controlled Differential Equations for Irregular Time Series Patrick Kidger James Morrill James Foster Terry Lyons AI4TS 90 470 0 18 May 2020
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series E. Brouwer Jaak Simm Adam Arany Yves Moreau SyDa CML AI4TS 91 295 0 29 May 2019
How do infinite width bounded norm networks look in function space? Pedro H. P. Savarese Itay Evron Daniel Soudry Nathan Srebro 72 165 0 13 Feb 2019
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 210 210 0 25 May 2017