
Out of Distribution Generalization in Machine Learning
Machine learning has achieved tremendous success in a variety of domains...
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Low Distortion BlockResampling with Spatially Stochastic Networks
We formalize and attack the problem of generating new images from old on...
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Never Give Up: Learning Directed Exploration Strategies
We propose a reinforcement learning agent to solve hard exploration game...
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Symplectic Recurrent Neural Networks
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algo...
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Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
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Geometrical Insights for Implicit Generative Modeling
Learning algorithms for implicit generative models can optimize a variet...
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Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, b...
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Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional G...
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Towards Principled Methods for Training Generative Adversarial Networks
The goal of this paper is not to introduce a single algorithm or method,...
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Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which join...
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Unitary Evolution Recurrent Neural Networks
Recurrent neural networks (RNNs) are notoriously difficult to train. Whe...
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Saddlefree Hessianfree Optimization
Nonconvex optimization problems such as the ones in training deep neural...
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Martin Arjovsky
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