View of Hyperspectral Image Compression using Modified Convolutional Autoencoder

Volterra Neural Networks (VNNs)

Understanding convolution on graphs via energies

Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is updated based on the information received from its neighbours. Most message-passing models act as...

Dual-windowed Vision Transformer with Angular Self-Attention

Following the great success in natural language processing, transformer-based models have emerged as the competitive model against the convolutional neural networks in computer vision. Vision...

Understanding convolution on graphs via energies

Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is updated based on the information received from its neighbours. Most message-passing models act as...

A Few Adversarial Tokens Can Break Vision Transformers

Vision transformers rely on self-attention operations between disjoint patches (tokens) of an input image, in contrast with standard convolutional networks. We investigate fundamental differences...

Transformer for Partial Differential Equations’ Operator Learning

Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions. The solution operators are...

Understanding Convolutional Neural Networks (CNN) with an example - The Triangle Agency

Click the link to discover all our marketing tools and unlimited access B2B email leads. Leads Vault After I completed Course 4 of the Coursera Deep Learning specialization, I wanted to give a brief summary to help you all understand and brush up on Convolutional Neural Net (CNN). Let’s take an example of CNNs – […]

Patches Are All You Need?

Although convolutional neural networks have been the dominant architecture for computer vision for many years, Vision Transformers (ViTs) have recently shown promise as an alternative. Subsequently...