A compact constraint incremental method for random weight networks and its application
Q Wang, W Dai, C Zhang, J Zhu… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Incremental random weight networks (IRWNs) face the issues of weak generalization and
complicated network structure. There is an important reason: the learning parameters of …
complicated network structure. There is an important reason: the learning parameters of …
Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective
In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit
from assessing alternative forecasting models of stock index movement. The present …
from assessing alternative forecasting models of stock index movement. The present …
Learning nonlinear projections for reduced-order modeling of dynamical systems using constrained autoencoders
Recently developed reduced-order modeling techniques aim to approximate nonlinear
dynamical systems on low-dimensional manifolds learned from data. This is an effective …
dynamical systems on low-dimensional manifolds learned from data. This is an effective …
Model sparsification for communication-efficient multi-party learning via contrastive distillation in image classification
KY Feng, M Gong, K Pan, H Zhao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Multi-party learning allows all parties to train a joint model under legal and practical
constraints without private data transmission. Related research can perform multi-party …
constraints without private data transmission. Related research can perform multi-party …
Discriminative feature learning and selection with label-induced sparse filtering for intelligent fault diagnosis of rotating machinery
Z Zhang, S Xu, H Chen - Mechanical Systems and Signal Processing, 2023 - Elsevier
Abstract Representation learning has demonstrated its powerful potential in intelligent fault
diagnosis of rotating machinery, where sparse filtering (SF) is a popular and promising …
diagnosis of rotating machinery, where sparse filtering (SF) is a popular and promising …
Multi-domain clustering pruning: Exploring space and frequency similarity based on GAN
Network compression plays an important role in accelerating deep neural networks,
especially in the application of edge devices such as unmanned cars and drones. Recently …
especially in the application of edge devices such as unmanned cars and drones. Recently …
Rasp: Regularization-based amplitude saliency pruning
Due to the prevalent data-dependent nature of existing pruning criteria, norm criteria with
data independence play a crucial role in filter pruning criteria, providing promising prospects …
data independence play a crucial role in filter pruning criteria, providing promising prospects …
Convergence Analysis of Online Gradient Method for High-Order Neural Networks and Their Sparse Optimization
In this article, we investigate the boundedness and convergence of the online gradient
method with the smoothing group regularization for the sigma-pi-sigma neural network …
method with the smoothing group regularization for the sigma-pi-sigma neural network …
Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images
Semantic segmentation of remote-sensing (RS) images is one of the most fundamental tasks
in the understanding of a remote-sensing scene. However, high-resolution RS images …
in the understanding of a remote-sensing scene. However, high-resolution RS images …
A multi-criteria approach to evolve sparse neural architectures for stock market forecasting
The development of machine learning based models to predict the movement of a financial
market has been a challenging problem due to the low signal-to-noise ratio under the effect …
market has been a challenging problem due to the low signal-to-noise ratio under the effect …