Results 51 to 60 of about 1,532,152 (301)

Seesaw-Net: Convolution Neural Network With Uneven Group Convolution

CoRR, 2019
In this paper, we are interested in boosting the representation capability of convolution neural networks which utilizing the inverted residual structure. Based on the success of Inverted Residual structure[Sandler et al. 2018] and Interleaved Low-Rank Group Convolutions[Sun et al.
openaire   +3 more sources

Simplicial 2-Complex Convolutional Neural Nets

, 2020
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not account for higher order relations between their hyperedges. Simplicial complexes offer a middle ground,
Bunch, Eric, You, Qian, Fung, Glenn, Singh, Vikas   +3 more
openaire   +2 more sources

Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning

, 2016
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains ...
Daniel E. Padilla, McDonnell, M.D., Mark D. McDonnell, Padilla, D.E.   +3 more
core   +1 more source

CN-Nets for modeling and analyzing neural networks [PDF]

, 2001
he concept of colored timed neural Petri nets (CTNPN or Shortly eN-net) which are isomorphic to neural architectures is proposed. The CN-net technique incorporates the basic features of the neural net and the modeling capabilities of both colored and ...
Samir M. Koriem, Koriem, Samir M.
core   +1 more source

Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis

Arthritis Care &Research, EarlyView.
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling, Ties van het Erve, Yvette van de Berg, Simon Mastbergen, Julia Spierings   +4 more
wiley   +1 more source

A self‐supervised causal feature reinforcement learning method for non‐invasive hemoglobin prediction

IET Image Processing
Anemia (hemoglobin (Hb) 
Linquan Xu, Yuwen Chen, Songmei Lu, Kunhua Zhong, Yujie Li, Bin Yi   +5 more
doaj   +1 more source

Convolutional Neural Networks–Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps

Cells, 2020
Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections.
Aneta Manda-Handzlik, Krzysztof Fiok, Adrianna Cieloch, Edyta Heropolitanska-Pliszka, Urszula Demkow   +4 more
doaj   +1 more source

BitFlow-Net: Toward Fully Binarized Convolutional Neural Networks [PDF]

IEEE Access, 2019
Binarization can greatly compress and accelerate deep convolutional neural networks (CNNs) for real-time industrial applications. However, existing binarized CNNs (BCNNs) rely on scaling factor (SF) and batch normalization (BatchNorm) that still involve resource-consuming floating-point multiplication operations.
Lijun Wu 0002, Peiqing Jiang, Zhicong Chen, Xu Lin, Yunfeng Lai, Peijie Lin, Shuying Cheng   +6 more
openaire   +2 more sources

Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks [PDF]

Communications in Computational Physics, 2020
Deep networks, especially convolutional neural networks (CNNs), have been successfully applied in various areas of machine learning as well as to challenging problems in other scientific and engineering fields. This paper introduces Butterfly-net, a low-complexity CNN with structured and sparse cross-channel connections, together with a Butterfly ...
Li, Y, Cheng, X, Lu, J
openaire   +2 more sources

Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction

, 2023
Spiking neural networks (SNNs) can be used in low-power and embedded systems e.g. neuromorphic chips due to their event-based nature. They preserve conventional artificial neural networks (ANNs) properties with lower computation and memory costs.
Fatemeh Sadat Tabatabaei Far, Nickabadi, Ahmad, Samadzadeh, Ali, Javadi, Ali, Far, Fatemeh Sadat Tabatabaei,, Samadzadeh, Ali,, Haghir Chehreghani, Morteza,, Haghir Chehreghani, Morteza, Ali Samadzadeh, Ahmad Nickabadi, Javadi, Ali,, Ali Javadi, Far, Fatemeh Sadat Tabatabaei, Nickabadi, Ahmad,, Morteza Haghir Chehreghani   +14 more
core   +1 more source