The Implicit Metropolis-Hastings Algorithm
Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
Neural Information Processing Systems http://nips.cc/
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Riemannian Laplace approximations for Bayesian neural networks
However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates.
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Learnable Tree Filter for Structure-preserving Feature Transform
Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun, Jian Sun, Nanning Zheng
Neural Information Processing Systems http://nips.cc/
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Toddler-Inspired Visual Object Learning
Sven Bambach, David Crandall, Linda Smith, Chen Yu
You Only Look Once: ime Object Detection.
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ef0c0a23a1a8219c4fc381614664df3e-Paper-Conference.pdf
Thefamily of f-divergences includes Kullback-Leibler divergence [23] and Total Variation distance.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S. Cohen
Neural Information Processing Systems http://nips.cc/
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Full-Gradient Representation for Neural Network Visualization
Suraj Srinivas, François Fleuret
Neural Information Processing Systems http://nips.cc/
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Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
In this paper we are concerned with investigating theperformance ofNN-based methods foranomaly detection. We firstshowthrough extensivesimulations thatNNmethods compare favorably to some of the other state-of-the-art algorithms for anomaly detection based on a setofbenchmark syntheticdatasets.
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