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AFIDAF: Alternating Fourier and Image Domain Adaptive Filters as an Efficient Alternative to Attention in ViTs
We propose and demonstrate an alternating Fourier and image domain filtering approach for feature extraction as an efficient alternative to build a vision backbone without using the computationally intensive attention.
A Proximal Algorithm for Network Slimming
This time-consuming, three-step process is a result of using subgradient descent to train CNNs.
Searching Intrinsic Dimensions of Vision Transformers
It has been shown by many researchers that transformers perform as well as convolutional neural networks in many computer vision tasks.
RARTS: An Efficient First-Order Relaxed Architecture Search Method
Differentiable architecture search (DARTS) is an effective method for data-driven neural network design based on solving a bilevel optimization problem.
Learning Sparse Neural Networks via $\ell_0$ and T$\ell_1$ by a Relaxed Variable Splitting Method with Application to Multi-scale Curve Classification
We study sparsification of convolutional neural networks (CNN) by a relaxed variable splitting method of $\ell_0$ and transformed-$\ell_1$ (T$\ell_1$) penalties, with application to complex curves such as texts written in different fonts, and words written with trembling hands simulating those of Parkinson's disease patients.
