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Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train.
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Towards deep learning models resistant to adversarial attacks
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Improved variational inference with inverse autoregressive flow
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The gan landscape: Losses, architectures, regularization, and normalization
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Spectral normalization for generative adversarial networks
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Lagging inference networks and posterior collapse in variational autoencoders
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