Guided Regularizers for Structured Reduction of Neural Networks
Traditional regularization techniques based on [Formula: see text] and [Formula: see text] norms of the weight vectors are widely used for sparsifying neural networks. However, the resulting sparsity patterns are scattered, as weights are pruned based solely on their magnitude, but without considera...
Saved in:
Main Authors: | Ali Haisam Muhammad Rafid, Adrian Sandu |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2025-12-01
|
Series: | Data Science in Science |
Subjects: | |
Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2025.2524558 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening
by: Zeinab Dehghan, et al.
Published: (2025-01-01) -
Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout
by: Mehdi Ghayoumi
Published: (2025-05-01) -
Noise Reduction of Depth Cameras Images Based on Deep Neural Network
by: Alireza Ghasemi, et al.
Published: (2024-02-01) -
A spiking photonic neural network of 40 000 neurons, trained with latency and rank-order coding for leveraging sparsity
by: Ria Talukder, et al.
Published: (2025-01-01) -
Neural networks : computers with intuition /
by: Brunak, S ren
Published: (1989)