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The Use of Stability Principle for Kernel Determination in Relevance Vector Machines.

D. Kropotov, D. Vetrov, N. Ptashko, и O. Vasiliev. ICONIP (1), том 4232 из Lecture Notes in Computer Science, стр. 727-736. Springer, (2006)

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Viatcheslav Vetrov

Dmitry Sveshnikov

Dmitry Suslov

Dmitry Varentsov

Dmitry Lobaskin

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Image Segmentation with a Shape Prior Based on Simplified Skeleton.B. Yangel, и D. Vetrov. EMMCVPR, том 6819 из Lecture Notes in Computer Science, стр. 247-260. Springer, (2011)One Approach to Fuzzy Expert Systems Construction.D. Kropotov, и D. Vetrov. ICEIS (2), стр. 566-570. (2003)Probabilistic Adaptive Computation Time.M. Figurnov, A. Sobolev, и D. Vetrov. CoRR, (2017)Pairwise Augmented GANs with Adversarial Reconstruction Loss.A. Alanov, M. Kochurov, D. Yashkov, и D. Vetrov. CoRR, (2018)Submodular Relaxation for Inference in Markov Random Fields.A. Osokin, и D. Vetrov. IEEE Trans. Pattern Anal. Mach. Intell., 37 (7): 1347-1359 (2015)Bayesian Incremental Learning for Deep Neural Networks.M. Kochurov, T. Garipov, D. Podoprikhin, D. Molchanov, A. Ashukha, и D. Vetrov. ICLR (Workshop), OpenReview.net, (2018)Few-shot Generative Modelling with Generative Matching Networks.S. Bartunov, и D. Vetrov. AISTATS, том 84 из Proceedings of Machine Learning Research, стр. 670-678. PMLR, (2018)ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks.I. Kemaev, D. Polykovskiy, и D. Vetrov. ACML, том 95 из Proceedings of Machine Learning Research, стр. 422-437. PMLR, (2018)Metropolis-Hastings view on variational inference and adversarial training.K. Neklyudov, P. Shvechikov, и D. Vetrov. CoRR, (2018)Semi-Conditional Normalizing Flows for Semi-Supervised Learning.A. Atanov, A. Volokhova, A. Ashukha, I. Sosnovik, и D. Vetrov. CoRR, (2019)

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