Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks [PDF]
, 1998 We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function.
Dereventsov, Anton +2 more
core +4 more sources
A 12-week cycling training regimen improves upper limb functions in people with Parkinson’s disease [PDF]
, 2018 Background: It has been proposed that physical exercise can help improve upper limb functions in Parkinson's disease (PD) patients; yet evidence for this hypothesis is limited.
Bherer, Louis +10 more
core +3 more sources
Aspects of Importance Sampling in Parameter Selection for Neural Networks Using Ridgelet Transform
Journal of the Physical Society of JapanThe choice of parameters in neural networks is crucial in the performance, and an oracle distribution derived from the ridgelet transform enables us to obtain suitable initial parameters. In other words, the distribution of parameters is connected to the integral representation of target functions.
Hikaru Homma, Jun Ohkubo
openaire +2 more sources
Ridgelet-based signature for natural image classification [PDF]
, 2005 This paper presents an approach to grouping natural scenes into (semantically) meaningful categories. The proposed approach exploits the statistics of natural scenes to define relevant image categories.
Le Borgne, Hervé, O'Connor, Noel E.
core
Deep Convolutional Neural Networks Based on Semi-Discrete Frames
, 2015 Deep convolutional neural networks have led to breakthrough results in practical feature extraction applications. The mathematical analysis of these networks was pioneered by Mallat, 2012.
Bölcskei, Helmut, Wiatowski, Thomas
core +1 more source
Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning [PDF]
, 2015 The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs).
Cheng, Gong +4 more
core +1 more source
Learning a Dilated Residual Network for SAR Image Despeckling
, 2018 In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a ...
Li, Jie +4 more
core +2 more sources
The ridgelet prior: A covariance function approach to prior specification for bayesian neural networks [PDF]
, 2021 Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated with the predictive output in the Bayesian framework.
Briol, FX, Matsubara, T, Oates, CJ
core
EnergiesThis paper designs a multi-variable hybrid islanding-detection method (HIDM) using signal-processing techniques. The signals of current captured on a test system where the renewable energy (RE) penetration level is between 50% and 100% are processed by ...
Ming Li +4 more
doaj +1 more source
Extraction of Face Features Using Various Techniques [PDF]
, 2014 This thesis aims at devising a novel method of feature extraction of face images which proves to be faster and more accurate than the existing methods defined by wavelet, curvelet and ridgelet transforms.
Biswal, Shruti
core