22 citations · 34 across the 8 of their papers we have counts for
8 papers
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3
Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal state, or developmental stag…
Linking convolutional kernel size to generalization bias in face analysis CNNs
Hao Liang, Josue Ortega Caro, Vikram Maheshri +2
Training dataset biases are by far the most scrutinized factors when explaining algorithmic biases of neural networks. In contrast, hyperparameters related to the neural network ar…
Continuous Spatiotemporal Transformers
Antonio H. de O. Fonseca, Emanuele Zappala, Josue Ortega Caro +1
Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide…
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
Syed Asad Rizvi, Nazreen Pallikkavaliyaveetil, David Zhang +15
Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstru…
Neural Integral Equations
Emanuele Zappala, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro +4
Nonlinear operators with long distance spatiotemporal dependencies are fundamental in modeling complex systems across sciences, yet learning these nonlocal operators remains challe…
Understanding robustness and generalization of artificial neural networks through Fourier masks
Nikos Karantzas, Emma Besier, Josue Ortega Caro +4
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generaliz…
Shallow Univariate ReLu Networks as Splines: Initialization, Loss Surface, Hessian, & Gradient Flow Dynamics
Justin Sahs, Ryan Pyle, Aneel Damaraju +4
Understanding the learning dynamics and inductive bias of neural networks (NNs) is hindered by the opacity of the relationship between NN parameters and the function represented. W…
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
Josue Ortega Caro, Yilong Ju, Ryan Pyle +4
Adversarial Attacks are still a significant challenge for neural networks. Recent work has shown that adversarial perturbations typically contain high-frequency features, but the r…