tags: Deep Learning, Machine Learning, Research, Top list, Yoshua Bengio, machine learning and Deep Learning research advances are transforming our technology. Learning to Execute - Wojciech Zaremba, Ilya Sutskever, 2015. Neural Attribute Machines for Program Generation - Matthew Amodio, Swarat Chaudhuri, Thomas Reps, 2017. Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction - Da Xiao, Jo-Yu Liao, cover letter for application for employment Xingyuan Yuan, iclr 2018. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. SentiCR: A Customized Sentiment Analysis Tool for Code Review Interactions - Toufique Ahmed, Amiangshu Bosu, Anindya Iqbal, Shahram Rahimi, ASE 2017. Lopes, Petr Maj, Pedro Martins, Vaibhav Saini, Di Yang, Jakub Zitny, Hitesh Sajnani, Jan Vitek, Programming Languages oopsla 2017. A deep language model for software code - Hoa Khanh Dam, Truyen Tran, Trang Pham, 2016.
Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. Dropout: a simple way to prevent neural networks from overfitting, by Hinton,.E., Krizhevsky,., Srivastava,., Sutskever,., Salakhutdinov,. Information Analysis Officer, Jackass.
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For some references, where CV is zero that means it was blank or not shown by semanticscholar. I'm coming for all your heads. Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets - Joulin, Armand, and Tomas Mikolov, nips 2015. A Language-Agnostic Model for Semantic Source Code Labeling - Ben Gelman, Bryan Hoyle, Jessica Moore, Joshua slavery is bad essay Saxe and David Slater, mases 2018. (cited 463 times, HIC: 55, CV: 0) Summary: 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 discriminative model D that estimates the. Neural Nets Can Learn Function Type Signatures From Binaries - Zheng Leong Chua, Shiqi Shen, Prateek Saxena, and Zhenkai Liang, usenix Security Symposium 2017. Hierarchical multiscale recurrent neural networks - Chung Junyoung, Sungjin Ahn, and Yoshua Bengio, iclr 2017. Deep Learning Type Inference - Vincent. Gaunt, Daniel Tarlow, 2017. Clone Digger - clone detection for Python and Java. Barr, Premkumar Devanbu, Charles Sutton, 2017.
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