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Recent advances in deep generative models have led to impressive results in a variety of application domains.
On Information and Sufficiency
Kullback, S. and Leibler, R. A · 1951
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Remarks on Some Nonparametric Estimates of a Density Function
Rosenblatt, M · 1956
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On Estimation of a Probability Density Function and Mode
Parzen, E · 1962
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The Influence Curve and Its Role in Robust Estimation
Hampel, F. R · 1974
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Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression
Cook, R. D. and Weisberg, S · 1980
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Long Short-Term Memory
Hochreiter, S. and Schmidhuber, J · 1997
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The MNIST Database of Handwritten Digits, 1998
LeCun, Y., Cortes, C., and Burges, C. J. C · 1998
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Stability and Generalization
Bousquet, O. and Elisseeff, A · 2002
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Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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On the Quantitative Analysis of Deep Belief Networks
Salakhutdinov, R. and Murray, I · 2008
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
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Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring
Quinn, J. A., Williams, C. K. I., and McIntosh, N · 2009
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Rectified Linear Units Improve Restricted Boltzmann Machines
Nair, V. and Hinton, G. E · 2010
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How Much is Enough? Choosing ε \varepsilon for Differential Privacy
Lee, J. and Clifton, C · 2011
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A Family of Nonparametric Density Estimation Algorithms
Tabak, E. G. and Turner, C. V · 2013
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RNADE: The Real-Valued Neural Autoregressive Density-Estimator
Uria, B., Murray, I., and Larochelle, H · 2013
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Rectifier Nonlinearities Improve Neural Network Acoustic Models
Maas, A. L., Hannun, A. Y., and Ng, A. Y · 2013
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Generative Adversarial Nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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The Algorithmic Foundations of Differential Privacy
Dwork, C. and Roth, A · 2014
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Differential Privacy: An Economic Method for Choosing Epsilon
Hsu, J., Gaboardi, M., Haeberlen, A., Khanna, S., Narayan, A., Pierce, B. C., and Roth, A · 2014
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Variational Inference with Normalizing Flows
Rezende, D. and Mohamed, S · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
Cited alongside, same era.
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
A Note on the Evaluation of Generative Models
Theis, L., van den Oord, A., and Bethge, M · 2016
Cited alongside, same era.
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheun, V., Radford, A., and Chen, X · 2016
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LOGAN: Membership Inference Attacks Against Generative Models
Hayes, J., Melis, L., Danezis, G., and De Cristofaro, E · 2019
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Detecting Overfitting of Deep Generative Networks via Latent Recovery
Webster, R., Rabin, J., Simon, L., and Jurie, F · 2019
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Do Deep Generative Models Know What They Don’t Know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
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Towards GAN Benchmarks Which Require Generalization
Gulrajani, I., Raffel, C., and Metz, L · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Importance Weighted Autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2016
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2016
Cited alongside, same era.
Understanding Deep Learning Requires Rethinking Generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
Cited alongside, same era.
A Closer Look at Memorization in Deep Networks
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., and Lacoste-Julien, S · 2017
Cited alongside, same era.
Revisiting Classifier Two-Sample Tests
Lopez-Paz, D. and Oquab, M · 2017
Cited alongside, same era.
Membership Inference Attacks against Machine Learning Models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
Akrami, H., Joshi, A. A., Li, J., Aydore, S., and Leahy, R. M · 2019
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Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-off
Belkin, M., Hsu, D., Ma, S., and Mandal, S · 2019
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
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NVAE: A Deep Hierarchical Variational Autoencoder
Vahdat, A. and Kautz, J · 2020
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Do We Train on Test Data? Purging CIFAR of Near-Duplicates
Barz, B. and Denzler, J · 2020
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What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Feldman, V. and Zhang, C · 2020
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Early-Learning Regularization Prevents Memorization of Noisy Labels
Liu, S., Niles-Weed, J., Razavian, N., and Fernandez-Granda, C · 2020
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A Non-Parametric Test to Detect Data-Copying in Generative Models
Meehan, C., Chaudhuri, K., and Dasgupta, S · 2020
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Estimating Training Data Influence by Tracing Gradient Descent
Pruthi, G., Liu, F., Kale, S., and Sundararajan, M · 2020
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Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data
Eduardo, S., Nazábal, A., Williams, C. K. I., and Sutton, C · 2020
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Deep Double Descent: Where Bigger Models and More Data Hurt
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I · 2020
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On the Geometry of Generalization and Memorization in Deep Neural Networks
Stephenson, C., Padhy, S., Ganesh, A., Hui, Y., Tang, H., and Chung, S · 2021
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Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Jiang, Z., Zhang, C., Talwar, K., and Mozer, M. C · 2021
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Robust Early-Learning: Hindering the Memorization of Noisy Labels
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., and Chang, Y · 2021
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Influence Estimation for Generative Adversarial Networks
Terashita, N., Ohashi, H., Nonaka, Y., and Kanemaru, T · 2021
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Influence Functions in Deep Learning Are Fragile
Basu, S., Pope, P., and Feizi, S · 2021
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Understanding Instance-based Interpretability of Variational Auto-Encoders
Kong, Z. and Chaudhuri, K · 2021
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Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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