GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications

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GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications

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Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

High-performance deep spiking neural networks with 0.3 spikes per neuron

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

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A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Multi-layer state evolution under random convolutional design

Topologically Better Delineation of Curvilinear Structures

Recurrent Neural Networks Are Universal Approximators With Stochastic Inputs

Universal Solution Manifold Networks (USM-Nets): Non-Intrusive Mesh-Free Surrogate Models for Problems in Variable Domains

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

On Breathing Pattern Information in Synthetic Speech

Geometry-Aware Deep Recurrent Neural Networks for Hyperspectral Image Classification

3D Structure From 2D Microscopy Images Using Deep Learning

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Multilingual Training and Adaptation in Speech Recognition

Detection of Bad PMU Data using Machine Learning Techniques

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

Machine learning methods to assist energy system optimization

Recurrent U-Net for Resource-Constrained Segmentation