[PDF][PDF] Dropout: a simple way to prevent neural networks from overfitting.

N Srivastava, GE Hinton, A Krizhevsky… - Journal of Machine …, 2014 - jmlr.org
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky, I Sutskever… - J. Mach. Learn. …, 2014 - citeulike.org
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - csri.utoronto.ca
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - Citeseer
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - jmlr.csail.mit.edu
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - cs.utoronto.ca
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - ailab.chonbuk.ac.kr
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - ailab.jbnu.ac.kr
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

[PDF][PDF] Dropout: A Simple Way to Prevent Neural Networks from Overfitting

N Srivastava, G Hinton, A Krizhevsky… - Journal of Machine …, 2014 - jmlr.org
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...

Dropout: a simple way to prevent neural networks from overfitting

N Srivastava, G Hinton, A Krizhevsky… - The Journal of Machine …, 2014 - dl.acm.org
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...