219 citations · 942 across the 36 of their papers we have counts for
7 papers · 1 filter
Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization
Leonel Aguilar, Jan Nagler, Christoph Hoelscher +1
Deep neural networks can learn salient features from an in-distribution (ID) training window without identifying the data-generating structure needed for out-of-distribution (OOD)…
Towards agent-based-model informed neural networks
Nino Antulov-Fantulin
In this article, we present a framework for designing neural networks that remain consistent with the underlying principles of agent-based models. We begin by highlighting the limi…
Implicit energy regularization of neural ordinary-differential-equation control
Lucas Böttcher, Nino Antulov-Fantulin, Thomas Asikis
Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and…
Neural Ordinary Differential Equation Control of Dynamics on Graphs
Thomas Asikis, Lucas Böttcher, Nino Antulov-Fantulin
We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent…
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights i…
Low-dimensional statistical manifold embedding of directed graphs
Thorben Funke, Tian Guo, Alen Lancic +1
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-lin…
Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Vaibhav Krishna, Tian Guo, Nino Antulov-Fantulin
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems. In recent times, different variants of deep learning algorith…