🔓 Public Access: Paper titles and abstracts are freely accessible.
Recent Papers
2016
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly refo...
2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and...
2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a dis...
2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed t...
2012
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achiev...
2014
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
Deep neural networks with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Dropout is a technique for addressing thi...