research
∙
02/07/2023
Towards causally linking architectural parametrizations to algorithmic bias in neural networks
Training dataset biases are by far the most scrutinized factors when exp...
share
research
∙
01/31/2023
Continuous Spatiotemporal Transformers
Modeling spatiotemporal dynamical systems is a fundamental challenge in ...
research
∙
10/17/2022
AMPNet: Attention as Message Passing for Graph Neural Networks
Feature-level interactions between nodes can carry crucial information f...
research
∙
09/30/2022
Neural Integral Equations
Integral equations (IEs) are functional equations defined through integr...
research
∙
03/16/2022
Understanding robustness and generalization of artificial neural networks through Fourier masks
Despite the enormous success of artificial neural networks (ANNs) in man...
research
∙
08/04/2020
Shallow Univariate ReLu Networks as Splines: Initialization, Loss Surface, Hessian, Gradient Flow Dynamics
Understanding the learning dynamics and inductive bias of neural network...
research
∙
06/19/2020
Using Learning Dynamics to Explore the Role of Implicit Regularization in Adversarial Examples
Recent work (Ilyas et al, 2019) suggests that adversarial examples are f...
research
∙
06/07/2017