A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
A comprehensive survey and analysis of generative models in machine learning
Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …
learning, we come across many scenarios when directly learning a target is intractable …
Review and comparison of commonly used activation functions for deep neural networks
T Szandała - Bio-inspired neurocomputing, 2021 - Springer
The primary neural networks' decision-making units are activation functions. Moreover, they
evaluate the output of networks neural node; thus, they are essential for the performance of …
evaluate the output of networks neural node; thus, they are essential for the performance of …
Activation functions: Comparison of trends in practice and research for deep learning
Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …
real world complex problems with may more deep learning (DL) architectures, being …
[图书][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …
McDonald Neural networks were developed to simulate the human nervous system for …
[HTML][HTML] Knowledge Discovery: Methods from data mining and machine learning
The interdisciplinary field of knowledge discovery and data mining emerged from a
necessity of big data requiring new analytical methods beyond the traditional statistical …
necessity of big data requiring new analytical methods beyond the traditional statistical …
A review on deep learning techniques for video prediction
S Oprea, P Martinez-Gonzalez… - … on Pattern Analysis …, 2020 - ieeexplore.ieee.org
The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …
intelligent decision-making systems. In light of the success of deep learning in computer …
Pixel recurrent neural networks
A Van Den Oord, N Kalchbrenner… - … on machine learning, 2016 - proceedings.mlr.press
Modeling the distribution of natural images is a landmark problem in unsupervised learning.
This task requires an image model that is at once expressive, tractable and scalable. We …
This task requires an image model that is at once expressive, tractable and scalable. We …
Importance weighted autoencoders
The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed
generative model pairing a top-down generative network with a bottom-up recognition …
generative model pairing a top-down generative network with a bottom-up recognition …