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About Khue

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  1. Semantic Segmentation using Fully Convolutional Networks over the years + PyTorch code

  2. RT @Deep_Hub: Guide to Semantic Segmentation with Deep Learning

  3. RT @iamtrask: Just came across perhaps the best Calculus textbook ever... why in the world is this not the industry standard?…

  4. Introducing the BAIR Blog

  5. RT @AgoniGrammi: 2016 #computervision impact factors are out! domain in great shape, all journals went up... https…

  6. RT @hardmaru: This is the magic SELU activation function.

  7. RT @MathJax: We just released v2.7.1! REMINDER end-of-life April 30, 2017 - check today's updates on…

  8. RT @abel_prize: Congrats to Yves Meyer @ENS ParisSaclay #Abelprize 2017 is yours welcome to @dnva1 in Oslo in May! @ENS_ParisSaclay hurray!…

  9. RT @SaclayCDS: Associate/Full Professor position Computer Vision dept Mathemetics @centralesupelec @UnivParisSaclay #datascience https://t…

  10. RT @stanfordnlp: Lecture notes for Stanford CS224N Natural Language Processing with Deep Learning are now on GitHub. Edits welcome! https:/…

  11. RT @AgoniGrammi: Great summer school on #machinelearning, #DataScience, #bigdata (28.08-01.09) @Polytechnique, @Uni…

  12. RT @AgoniGrammi: Thumbs up @netw0rkf10w! #cvpr2017 graph matching paper smashes state of the art: #computervision,#…

  13. In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions. Unlike previous decomposition approaches that rely on the graph structures, we introduce a decomposition of the matching constraints. Graph matching is then reformulated as a non-convex non-separable optimization problem that can be split into smaller and much-easier-to-solve subproblems, by means of the alternating direction method of multipliers. The proposed framework is modular, scalable, and can be instantiated into different variants. Two instantiations are studied exploring pairwise and higher-order constraints. Experimental results on widely adopted benchmarks involving synthetic and real examples demonstrate that the proposed solutions outperform existing pairwise graph matching methods, and competitive with the state of the art in higher-order settings. D. Khuê Lê-Huu and Nikos Paragios. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. arXiv version
  14. RT @ericjang11: 1/ Google Brain doesn't do research, says Yann LeCun.

  15. RT @hardmaru: Definitely the most Controversial ICLR review by far.