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Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains.
Dropout: a simple way to prevent neural networks from overfitting
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Random decision forests
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Physiobank, physiotoolkit, and physionet
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Policy gradient methods for reinforcement learning with function approximation
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Machine learning: a probabilistic perspective
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A. (2013) · 2013
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Activation of liver x receptors inhibits hedgehog signaling, clonogenic growth, and self-renewal in multiple myeloma
Agarwal, J. R., Wang, Q., Tanno, T., Rasheed, Z., Merchant, A., Ghosh, N., Borrello, I., Huff, C. A., Parhami, F., and Matsui, W. (2014) · 2014
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Adam: A method for stochastic optimization
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M. (2014) · 2014
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Maeda, S.-i. (2014) · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R. (2014) · 2014
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S. (2015) · 2015
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Distilling knowledge from deep networks with applications to healthcare domain
Che, Z., Purushotham, S., Khemani, R., and Liu, Y. (2015) · 2015
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A proactive intelligent decision support system for predicting the popularity of online news
Fernandes, K., Vinagre, P., and Cortez, P. (2015) · 2015
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Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M. (2015) · 2015
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The human splicing code reveals new insights into the genetic determinants of disease
Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K., Hua, Y., Gueroussov, S., Najafabadi, H. S., Hughes, T. R., et al · 2015
Proteasome inhibitor-adapted myeloma cells are largely independent from proteasome activity and show complex proteomic changes, in particular in redox and energy metabolism
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016) · 2016
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Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y. (2017) · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017) · 2017
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Interpretable explanations of black boxes by meaningful perturbation
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Recurrent neural networks for multivariate time series with missing values
Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y. (2016) · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2016) · 2016
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Deep feature selection: theory and application to identify enhancers and promoters
Li, Y., Chen, C.-Y., and Wasserman, W. W. (2016) · 2016
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Modeling missing data in clinical time series with rnns
Lipton, Z. C., Kale, D. C., and Wetzel, R. (2016) · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W. (2016) · 2016
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Safs: A deep feature selection approach for precision medicine
Nezhad, M. Z., Zhu, D., Li, X., Yang, K., and Levy, P. (2016) · 2016
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
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Fong, R. and Vedaldi, A. (2017) · 2017
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An improved multi-output gaussian process rnn with real-time validation for early sepsis detection
Futoma, J., Hariharan, S., Sendak, M., Brajer, N., Clement, M., Bedoya, A., O’Brien, C., and Heller, K. (2017) · 2017
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Gal, Y., Hron, J., and Kendall, A. (2017) · 2017
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Variational dropout sparsifies deep neural networks
Molchanov, D., Ashukha, A., and Vetrov, D. (2017) · 2017
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Cardiologist-level arrhythmia detection with convolutional neural networks
Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., and Ng, A. Y. (2017) · 2017
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Dr.VAE: Drug Response Variational Autoencoder
Rampasek, L., Hidru, D., Smirnov, P., Haibe-Kains, B., and Goldenberg, A. (2017) · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A. (2017) · 2017
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Clinical intervention prediction and understanding using deep networks
Suresh, H., Hunt, N., Johnson, A., Celi, L. A., Szolovits, P., and Ghassemi, M. (2017) · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M. (2017) · 2017
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