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Home2020June

Dominate or Delete: Decentralized Competing Bandits with Uniform Valuation. (arXiv:2006.15166v1 [cs.LG])

Science, Statistics June 30, 2020 E-print Repository

We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms) with

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Deep Involutive Generative Models for Neural MCMC. (arXiv:2006.15167v1 [stat.ML])

Science, Statistics June 30, 2020 E-print Repository

We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC. An involutive

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Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings. (arXiv:2006.15168v1 [stat.ML])

Science, Statistics June 30, 2020 E-print Repository

Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We take a

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A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications. (arXiv:2006.15172v1 [cs.LG])

Science, Statistics June 30, 2020 E-print Repository

A key to ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to

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Interpretable Factorization for Neural Network ECG Models. (arXiv:2006.15189v1 [cs.LG])

Science, Statistics June 30, 2020 E-print Repository

The ability of deep learning (DL) to improve the practice of medicine and its clinical outcomes faces a looming obstacle: model interpretation. Without description of how outputs are generated, a

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Is SGD a Bayesian sampler? Well, almost. (arXiv:2006.15191v1 [cs.LG])

Science, Statistics June 30, 2020 E-print Repository

Overparameterised deep neural networks (DNNs) are highly expressive and so can, in principle, generate almost any function that fits a training dataset with zero error. The vast majority of these

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The gas, metal and dust evolution in low-metallicity local and high-redshift galaxies. (arXiv:2006.15146v1 [astro-ph.GA])

Astrophysics, Science June 30, 2020 E-print Repository

The chemical enrichment in the interstellar medium (ISM) of galaxies is regulated by several physical processes: stellar evolution, grain formation and destruction, galactic inflows and outflows. Understanding such processes is

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CMacIonize 2.0: a novel task-based approach to Monte Carlo radiation transfer. (arXiv:2006.15147v1 [astro-ph.IM])

Astrophysics, Science June 30, 2020 E-print Repository

(Context) Monte Carlo radiative transfer (MCRT) is a widely used technique to model the interaction between radiation and a medium, and plays an important role in astrophysical modelling and when

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Split SIMPs with Decays. (arXiv:2006.15148v1 [hep-ph])

Astrophysics, Science June 30, 2020 E-print Repository

We discuss a minimal realization of the strongly interacting massive particle (SIMP) framework. The model includes a dark copy of QCD with three colors and three light flavors. A massive

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C19-TraNet: an empirical, global index-case transmission network of SARS-CoV-2. (arXiv:2006.15162v1 [q-bio.PE])

Quantitative, Quantitative Biology, Science June 30, 2020 E-print Repository

Originating in Wuhan, the novel coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2), has astonished health-care systems across globe due to its rapid and simultaneous spread to the neighboring and distantly

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Recent Posts

  • Computing the Discrete Fourier Transform of signals with spectral frequency support. (arXiv:2102.12514v1 [eess.SP]) February 26, 2021
  • Prior Image-Constrained Reconstruction using Style-Based Generative Models. (arXiv:2102.12525v1 [eess.IV]) February 26, 2021
  • Estimation and Distributed Eradication of SIR Epidemics on Networks. (arXiv:2102.12549v1 [eess.SY]) February 26, 2021
  • Energy-consistent finite difference schemes for compressible hydrodynamics and magnetohydrodynamics using nonlinear filtering. (arXiv:2102.12476v1 [physics.comp-ph]) February 26, 2021
  • Nested sampling with any prior you like. (arXiv:2102.12478v1 [astro-ph.IM]) February 26, 2021

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2 3 A. Alfonso Albero A. Artamonov A. Bay ALICE Collaboration ATLAS Collaboration B. Adeva C.A. Aidala C. Abellán Beteta CMS Collaboration E. Aslanides F. Alessio F. Archilli F.~Bianchi G. Alkhazov Germany I. Bediaga I. Belyaev J. Albrecht J.J. Back K. Azizi K. Belous LHCb collaboration: R. Aaij M. Adinolfi M. Alexander M. Artuso M. Atzeni M. Bartolini P. Alvarez Cartelle R. A. Briere R.J. Barlow Raghunath Sahoo S. Akar S. Amato S. Bachmann S. Barsuk Ulf-G. Meißner USA V. Balagura W. Baldini W. Barter Wei Wang Y. Amhis Z. Ajaltouni

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