Convolutional neural networks as a model of the visual system: Past, present, and future
GW Lindsay - Journal of cognitive neuroscience, 2021 - direct.mit.edu
Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …
biological vision. They have since become successful tools in computer vision and state-of …
Deep learning for remote sensing data: A technical tutorial on the state of the art
Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …
a hierarchical manner from the data, have recently become a hotspot in the machine …
Deepsecure: Scalable provably-secure deep learning
This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL)
framework that is built upon automated design, efficient logic synthesis, and optimization …
framework that is built upon automated design, efficient logic synthesis, and optimization …
Lung nodule detection in CT images using deep convolutional neural networks
R Golan, C Jacob, J Denzinger - 2016 international joint …, 2016 - ieeexplore.ieee.org
Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great
importance for the successful diagnosis and treatment of lung cancer. Due to improvements …
importance for the successful diagnosis and treatment of lung cancer. Due to improvements …
Gearbox fault identification and classification with convolutional neural networks
ZQ Chen, C Li, RV Sanchez - Shock and Vibration, 2015 - Wiley Online Library
Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration
signals, this paper presents an implementation of deep learning algorithm convolutional …
signals, this paper presents an implementation of deep learning algorithm convolutional …
Deep sparse rectifier neural networks
While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent
neurons, the latter work better for training multi-layer neural networks. This paper shows that …
neurons, the latter work better for training multi-layer neural networks. This paper shows that …
Deep neural networks rival the representation of primate IT cortex for core visual object recognition
The primate visual system achieves remarkable visual object recognition performance even
in brief presentations, and under changes to object exemplar, geometric transformations …
in brief presentations, and under changes to object exemplar, geometric transformations …
Deep learning of representations: Looking forward
Y Bengio - International conference on statistical language and …, 2013 - Springer
Deep learning research aims at discovering learning algorithms that discover multiple levels
of distributed representations, with higher levels representing more abstract concepts …
of distributed representations, with higher levels representing more abstract concepts …
On the complexity of neural network classifiers: A comparison between shallow and deep architectures
M Bianchini, F Scarselli - IEEE transactions on neural networks …, 2014 - ieeexplore.ieee.org
Recently, researchers in the artificial neural network field have focused their attention on
connectionist models composed by several hidden layers. In fact, experimental results and …
connectionist models composed by several hidden layers. In fact, experimental results and …