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Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging.
Asymptotic evaluation of certain markov process expectations for large time. iv
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The im algorithm: A variational approach to information maximization
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Estimation of entropy and mutual information
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Kraskov, A., Stögbauer, H., and Grassberger, P · 2004
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Entropy and information in neural spike trains: Progress on the sampling problem
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Discriminative clustering by regularized information maximization
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
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Detecting novel associations in large data sets
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Doersch, C., Gupta, A., and Efros, A. A · 2015
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Efficient estimation of mutual information for strongly dependent variables
Gao, S., Ver Steeg, G., and Galstyan, A · 2015
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Deep learning and the information bottleneck principle
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Early visual concept learning with unsupervised deep learning
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Variational optimal experiment design: Efficient automation of adaptive experiments
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Entropy and mutual information in models of deep neural networks
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Learning deep representations by mutual information estimation and maximization
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Poole, B., Alemi, A. A., Sohl-Dickstein, J., and Angelova, A · 2016
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A review of modern computational algorithms for bayesian optimal design
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Fixing a broken elbo, 2017
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Variational inference: A review for statisticians
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Learning discrete representations via information maximizing self-augmented training
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Nonlinear information bottleneck
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Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Trischler, A., and Bengio, Y · 2018
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Kim, H. and Mnih, A · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
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Ma, Z. and Collins, M · 2018
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Disentangling disentanglement in variational auto-encoders, 2018
Mathieu, E., Rainforth, T., Siddharth, N., and Teh, Y. W · 2018
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Formal limitations on the measurement of mutual information, 2018
McAllester, D. and Stratos, K · 2018
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Invariant representations without adversarial training
Moyer, D., Gao, S., Brekelmans, R., Galstyan, A., and Ver Steeg, G · 2018
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Peng, X. B., Kanazawa, A., Toyer, S., Abbeel, P., and Levine, S · 2018
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Minimally redundant laplacian eigenmaps
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On nesting monte carlo estimators
Rainforth, T., Cornish, R., Yang, H., and Warrington, A · 2018
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On the information bottleneck theory of deep learning
Saxe, A. M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B. D., and Cox, D. D · 2018
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Vae with a vampprior
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Representation learning with contrastive predictive coding
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