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Bayesian Inference of Networks Across Multiple Sample Groups and Data Types. (arXiv:1909.02058v1 [stat.ME]) arxiv.org/abs/1909.02058

DCGANs for Realistic Breast Mass Augmentation in X-ray Mammography. (arXiv:1909.02062v1 [eess.IV]) arxiv.org/abs/1909.02062

Diversity Breeds Innovation With Discounted Impact and Recognition. (arXiv:1909.02063v1 [cs.SI]) arxiv.org/abs/1909.02063

On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors. (arXiv:1909.02088v1 [math.ST]) arxiv.org/abs/1909.02088

Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra. (arXiv:1909.02093v1 [q-bio.QM]) arxiv.org/abs/1909.02093

Accelerated Information Gradient flow. (arXiv:1909.02102v1 [math.OC]) arxiv.org/abs/1909.02102

Meta Learning with Relational Information for Short Sequences. (arXiv:1909.02105v1 [cs.LG]) arxiv.org/abs/1909.02105

Lund jet images from generative and cycle-consistent adversarial networks. (arXiv:1909.01359v1 [hep-ph]) arxiv.org/abs/1909.01359

Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings. (arXiv:1909.01401v1 [cs.LG]) arxiv.org/abs/1909.01401

Mixture Probabilistic Principal GeodesicAnalysis. (arXiv:1909.01412v1 [cs.LG]) arxiv.org/abs/1909.01412

Discriminative Topic Modeling with Logistic LDA. (arXiv:1909.01436v1 [stat.ML]) arxiv.org/abs/1909.01436

LCA: Loss Change Allocation for Neural Network Training. (arXiv:1909.01440v1 [cs.LG]) arxiv.org/abs/1909.01440

Interpretable Word Embeddings via Informative Priors. (arXiv:1909.01459v1 [cs.CL]) arxiv.org/abs/1909.01459

Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers. (arXiv:1909.01463v1 [cs.HC]) arxiv.org/abs/1909.01463

Rates of Convergence for Large-scale Nearest Neighbor Classification. (arXiv:1909.01464v1 [math.ST]) arxiv.org/abs/1909.01464

Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data. (arXiv:1909.01486v1 [cs.LG]) arxiv.org/abs/1909.01486

Minimum $L^q$-distance estimators for non-normalized parametric models. (arXiv:1909.00002v1 [math.ST]) arxiv.org/abs/1909.00002

A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in between. (arXiv:1909.00021v1 [cs.LG]) arxiv.org/abs/1909.00021

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