Training Generative Adversarial Networks with Limited Data. (arXiv:2006.06676v1 [cs.CV]) http://arxiv.org/abs/2006.06676
On the asymptotics of wide networks with polynomial activations. (arXiv:2006.06687v1 [cs.LG]) http://arxiv.org/abs/2006.06687
End-to-end Sinkhorn Autoencoder with Noise Generator. (arXiv:2006.06704v1 [cs.LG]) http://arxiv.org/abs/2006.06704
Optimizing generalization on the train set: a novel gradient-based framework to train parameters and hyperparameters simultaneously. (arXiv:2006.06705v1 [cs.LG]) http://arxiv.org/abs/2006.06705
Extreme data compression while searching for new physics. (arXiv:2006.06706v1 [astro-ph.CO]) http://arxiv.org/abs/2006.06706
Learning to Learn Kernels with Variational Random Features. (arXiv:2006.06707v1 [cs.LG]) http://arxiv.org/abs/2006.06707
A new measure for overfitting and its implications for backdooring of deep learning. (arXiv:2006.06721v1 [cs.LG]) http://arxiv.org/abs/2006.06721
Deep Reinforcement Learning for Electric Transmission Voltage Control. (arXiv:2006.06728v1 [cs.LG]) http://arxiv.org/abs/2006.06728
Weak variable step-size Euler schemes for stochastic differential equations based on controlling conditional moments. (arXiv:2006.06729v1 [math.PR]) http://arxiv.org/abs/2006.06729
Higher-order interactions in statistical physics and machine learning: A non-parametric solution to the inverse problem. (arXiv:2006.06010v1 [stat.ME]) http://arxiv.org/abs/2006.06010
Convergence of Pseudo-Bayes Factors in Forward and Inverse Regression Problems. (arXiv:2006.06020v1 [math.ST]) http://arxiv.org/abs/2006.06020
Learning normalizing flows from Entropy-Kantorovich potentials. (arXiv:2006.06033v1 [cs.LG]) http://arxiv.org/abs/2006.06033
On the Maximum Mutual Information Capacity of Neural Architectures. (arXiv:2006.06037v1 [cs.LG]) http://arxiv.org/abs/2006.06037
Efficient Contextual Bandits with Continuous Actions. (arXiv:2006.06040v1 [cs.LG]) http://arxiv.org/abs/2006.06040
On Mixup Regularization. (arXiv:2006.06049v1 [cs.LG]) http://arxiv.org/abs/2006.06049
Learning to Incentivize Other Learning Agents. (arXiv:2006.06051v1 [cs.LG]) http://arxiv.org/abs/2006.06051
Fair Data Integration. (arXiv:2006.06053v1 [cs.LG]) http://arxiv.org/abs/2006.06053
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions. (arXiv:2006.06057v1 [cs.LG]) http://arxiv.org/abs/2006.06057
Deterministic Gaussian Averaged Neural Networks. (arXiv:2006.06061v1 [cs.LG]) http://arxiv.org/abs/2006.06061
Conditional probability and improper priors. (arXiv:2006.04797v1 [math.ST]) http://arxiv.org/abs/2006.04797
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