Výsledky vyhledávání - "Lim, Yongsub"

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Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction

Autor: Yoon, Andre S., Lee, Taehoon, Lim, Yongsub, Jung, Deokwoo, Kang, Philgyun, Kim, Dongwon, Park, Keuntae, Choi, Yongjin

This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear

Externí odkaz: http://arxiv.org/abs/1709.00845

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FURL: Fixed-memory and Uncertainty Reducing Local Triangle Counting for Graph Streams

Autor: Jung, Minsoo, Lee, Sunmin, Lim, Yongsub, Kang, U

How can we accurately estimate local triangles for all nodes in simple and multigraph streams? Local triangle counting in a graph stream is one of the most fundamental tasks in graph mining with important applications including anomaly detection and

Externí odkaz: http://arxiv.org/abs/1611.06615

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Efficient Energy Minimization for Enforcing Statistics

Autor: Lim, Yongsub, Jung, Kyomin, Kohli, Pushmeet

Energy minimization algorithms, such as graph cuts, enable the computation of the MAP solution under certain probabilistic models such as Markov random fields. However, for many computer vision problems, the MAP solution under the model is not the gr

Externí odkaz: http://arxiv.org/abs/1307.7800

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Multi-dimensional Parametric Mincuts for Constrained MAP Inference

Autor: Lim, Yongsub, Jung, Kyomin, Kohli, Pushmeet

In this paper, we propose novel algorithms for inferring the Maximum a Posteriori (MAP) solution of discrete pairwise random field models under multiple constraints. We show how this constrained discrete optimization problem can be formulated as a mu

Externí odkaz: http://arxiv.org/abs/1307.7793

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