Fetching the paper…
Reading the bibliography…
While deep learning has achieved great success in many fields, one common criticism about deep learning is its lack of interpretability.
Probabilistic graphical models: principles and techniques
Koller, D., Friedman, N., and Bach, F · 2009
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Efficient learning of deep boltzmann machines
Salakhutdinov, R. and Larochelle, H · 2010
Earlier work this paper cites.
Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using paradigm
Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., and Stuart, J. M · 2010
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Cited alongside, same era.
Deepcare: A deep dynamic memory model for predictive medicine
Pham, T., Tran, T., Phung, D., and Venkatesh, S · 2016
Cited alongside, same era.
Explainable artificial intelligence (xai)
Gunning, D · 2017
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Later among the works it cites.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Later among the works it cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Later among the works it cites.
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Later among the works it cites.
Relational inductive biases, deep learning, and graph networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning for identifying metastatic breast cancer
Wang, D., Khosla, A., Gargeya, R., Irshad, H., and Beck, A. H · 2016
Cited alongside, same era.
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
Later among the works it cites.
An explainable deep machine vision framework for plant stress phenotyping
Ghosal, S., Blystone, D., Singh, A. K., Ganapathysubramanian, B., Singh, A., and Sarkar, S · 2018
Later among the works it cites.