Deep learning in microscopy image analysis: A survey
Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
On interpretability of artificial neural networks: A survey
Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …
successes recently in many important areas that deal with text, images, videos, graphs, and …
Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics
In numerous industrial applications where safety, efficiency, and reliability are among
primary concerns, condition-based maintenance (CBM) is often the most effective and …
primary concerns, condition-based maintenance (CBM) is often the most effective and …
Benefits of depth in neural networks
M Telgarsky - Conference on learning theory, 2016 - proceedings.mlr.press
For any positive integer k, there exist neural networks with Θ (k^ 3) layers, Θ (1) nodes per
layer, and Θ (1) distinct parameters which can not be approximated by networks with O (k) …
layer, and Θ (1) distinct parameters which can not be approximated by networks with O (k) …
Resnet with one-neuron hidden layers is a universal approximator
We demonstrate that a very deep ResNet with stacked modules that have one neuron per
hidden layer and ReLU activation functions can uniformly approximate any Lebesgue …
hidden layer and ReLU activation functions can uniformly approximate any Lebesgue …
Deep neural network for structural prediction and lane detection in traffic scene
Hierarchical neural networks have been shown to be effective in learning representative
image features and recognizing object classes. However, most existing networks combine …
image features and recognizing object classes. However, most existing networks combine …
Change detection in SAR images using deep belief network: a new training approach based on morphological images
F Samadi, G Akbarizadeh, H Kaabi - IET Image Processing, 2019 - Wiley Online Library
In solving change detection problem, unsupervised methods are usually preferred to their
supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this …
supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this …
An efficient approach for polyps detection in endoscopic videos based on faster R-CNN
Polyp has long been considered as one of the major etiologies to colorectal cancer which is
a fatal disease around the world, thus early detection and recognition of polyps plays an …
a fatal disease around the world, thus early detection and recognition of polyps plays an …
Continuous dropout
Dropout has been proven to be an effective algorithm for training robust deep networks
because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors …
because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors …
Sharp bounds for the number of regions of maxout networks and vertices of minkowski sums
We present results on the number of linear regions of the functions that can be represented
by artificial feedforward neural networks with maxout units. A rank-maxout unit is a function …
by artificial feedforward neural networks with maxout units. A rank-maxout unit is a function …