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We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner.
The properties of known drugs. 1. molecular frameworks
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Integral probability metrics and their generating classes of functions
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Prox-method with rate of convergence o (1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems
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An introduction to frames
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Extended-connectivity fingerprints
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Matched molecular pairs as a medicinal chemistry tool: miniperspective
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Scikit-learn: Machine learning in python
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Auto-encoding variational bayes
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The scored society: due process for automated predictions
Citron, D. K. and Pasquale, F · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2014
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C4. 5: programs for machine learning
Quinlan, J. R · 2014
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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The black box society: The secret algorithms that control money and information
Pasquale, F · 2015
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The sample complexity of learning linear predictors with the squared loss
Shamir, O · 2015
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Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
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Tensorflow: a system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Discriminative embeddings of latent variable models for structured data
Dai, H., Dai, B., and Song, L · 2016
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Interpretable decision sets: A joint framework for description and prediction
Lakkaraju, H., Bach, S. H., and Leskovec, J · 2016
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Bearing data set
Lee, J., Qiu, H., Yu, G., Lin, J., and Rexnord Technical Services (2007). IMS, U. o. C · 2016
The cramer distance as a solution to biased wasserstein gradients
Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., and Munos, R · 2017
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Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., and Usunier, N · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Orthogonal recurrent neural networks with scaled cayley transform
Helfrich, K., Willmott, D., and Ye, Q · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Rationalizing Neural Predictions
Lei, T., Barzilay, R., and Jaakkola, T · 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
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Contextual explanation networks
Al-Shedivat, M., Dubey, A., and Xing, E. P · 2017
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
Alvarez-Melis, D. and Jaakkola, T. S · 2017
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Deriving neural architectures from sequence and graph kernels
Lei, T., Jin, W., Barzilay, R., and Jaakkola, T · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Learning sleep stages from radio signals: a conditional adversarial architecture
Zhao, M., Yue, S., Katabi, D., Jaakkola, T. S., and Bianchi, M. T · 2017
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Game-theoretic interpretability for temporal modeling
Lee, G.-H., Alvarez-Melis, D., and Jaakkola, T. S · 2018
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When recurrent models don’t need to be recurrent
Miller, J. and Hardt, M · 2018
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Sobolev gan
Mroueh, Y., Li, C.-L., Sercu, T., Raj, A., and Cheng, Y · 2018
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Beyond sparsity: Tree regularization of deep models for interpretability
Wu, M., Hughes, M. C., Parbhoo, S., Zazzi, M., Roth, V., and Doshi-Velez, F · 2018
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Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J · 2019
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