Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Ruiyuan Lin"'
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::812eccf1024370fbe487e0a9234f8356
https://doi.org/10.1109/icassp49357.2023.10095417
https://doi.org/10.1109/icassp49357.2023.10095417
Publikováno v:
IEEE transactions on neural networks and learning systems.
The multilayer perceptron (MLP) neural network is interpreted from the geometrical viewpoint in this work, that is, an MLP partition an input feature space into multiple nonoverlapping subspaces using a set of hyperplanes, where the great majority of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8ee80158adffe036780d500e73afd00
https://pubmed.ncbi.nlm.nih.gov/35862331
https://pubmed.ncbi.nlm.nih.gov/35862331
Publikováno v:
IEEE Signal Processing Letters. 28:1813-1817
The relationship between a multilayer perceptron (MLP) regressor and a piecewise polynomial approximator is investigated in this work. We propose an MLP construction method, including the choice of activation, the specification of neuron numbers and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::382c80b248f81e93fa7af60f6ef36f4f
https://doi.org/10.1109/lsp.2021.3103130
https://doi.org/10.1109/lsp.2021.3103130
Autor:
René R. Sevag Packard, Yanan Fei, Dengfeng Kuang, Kyung In Baek, Tzung K. Hsiai, Zhaoqiang Wang, Mehrdad Roustaei, Varun Gudapati, Sibo Song, Ruiyuan Lin, C.-C. Jay Kuo, Yichen Ding, Chih-Chiang Chang
Publikováno v:
IEEE Trans Biomed Eng
IEEE transactions on bio-medical engineering, vol 68, iss 1
IEEE transactions on bio-medical engineering, vol 68, iss 1
Objective: Recent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto . However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fe0cdb9fc5bd2702c5ab8051d7ba8c6
https://doi.org/10.1109/tbme.2020.2991754
https://doi.org/10.1109/tbme.2020.2991754
Publikováno v:
Applications of Digital Image Processing XLIII.
Small neural networks (NNs) that have a small model size find applications in mobile and wearable computing. One famous example is the SqueezeNet that achieves the same accuracy as the AlexNet yet has 50x fewer parameters than AlexNet. A few follow-u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a311bc4f015c192da4367b19016b2e62
https://doi.org/10.1117/12.2567544
https://doi.org/10.1117/12.2567544
Autor:
Dengfeng Kuang, Mehrdad Roustaei, Varun Gudapati, Yichen Ding, Chih-Chiang Chang, Ruiyuan Lin, Tzung K. Hsiai, Yanan Fei, C.-C. Jay Kuo, Zhaoqiang Wang, Kyung In Baek, Sibo Song
Recent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a challenge. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f4af5822098934c09204177263c4eb9
A PCA based sequence-to-vector (seq2vec) dimension reduction method for the text classification problem, called the tree-structured multi-stage principal component analysis (TMPCA) is presented in this paper. Theoretical analysis and applicability of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58d8039b40b84c4ea525dfc797b0a262
http://arxiv.org/abs/1807.08228
http://arxiv.org/abs/1807.08228
Publikováno v:
APSIPA Transactions on Signal and Information Processing. 7
Trained features of a convolution neural network (CNN) at different convolution layers is analyzed using two quantitative metrics in this work. We first show mathematically that the Gaussian confusion measure (GCM) can be used to identify the discrim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2e308097494c92bae9faabb93e0d0d4
https://doi.org/10.1017/atsip.2018.24
https://doi.org/10.1017/atsip.2018.24
Publikováno v:
APSIPA
Two quantitative metrics are proposed for evaluating trained deep features at different convolution layers in this work. We first show mathematically that the Gaussian confusion measure (GCM) can be used to identify the discriminative ability of an i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b70cdee5da4f6b2c94ccfa5318446ff
https://doi.org/10.1109/apsipa.2017.8282184
https://doi.org/10.1109/apsipa.2017.8282184
Publikováno v:
APSIPA
A novel Convolutional Neural Network (CNN) solution is presented in this work to achieve human age/gender classification from their facial image data in an unconstrained environment. Our proposed Whole-Component CNN solution contains both the whole f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b7635220dfcb28e1938ec6babdf6540
https://doi.org/10.1109/apsipa.2017.8282221
https://doi.org/10.1109/apsipa.2017.8282221