2218 results for "earn" across all locations
Metropolis-Hastings Generative Adversarial Networks
R. Turner, J. Hung, Y. Saatci, J. Yosinski
We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distribution implicitly defined by a GAN’s discriminator-generator pair, as opposed to sampling in a standard GAN which draws samples from the distribution defined by the generator. […] [PDF]
International Conference on Machine Learning (ICML), 2019
Variational Bayesian dropout: pitfalls and fixes
J. Hron, A. Matthews, Z. Ghahramani
Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian neural networks. The main contribution of the reinterpretation is in providing a theoretical framework useful for analysing and extending the algorithm […] [PDF]
International Conference on Machine Learning (ICML), 2018
Synthesizing Entity Matching Rules by Examples
R. Singh, V. Vamsikrishna Meduri, A. Elmagarmid, S. Madden, P. Papotti, Jo. Quiané-Ruiz, A. Solar-Lezama, N. Tang
Entity matching (EM) is a critical part of data integration. We study how to synthesize entity matching rules from positive-negative matching examples. The core of our solution is program synthesis, a powerful tool to automatically generate rules (or programs) that satisfy a given highlevel specification, via a predefined grammar. […] [PDF]
Proceedings of the VLDB Endowment (PVLDB) 11(2): 189-202, 2017

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