• Content count

  • Joined

  • Last visited

Community Reputation

86 Excellent

1 Follower

About Khue

  • Rank
    Advanced Member

Personal Information

Recent Profile Visitors

10,376 profile views
  1. RT @MathJax: We just released v2.7.1! REMINDER end-of-life April 30, 2017 - check today's updates on…

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

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

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

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

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

  7. 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
  8. RT @ericjang11: 1/ Google Brain doesn't do research, says Yann LeCun.

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

  10. RT @soumithchintala: Wasserstein GANs pretty aptly summarized in this reddit comment:

  11. RT @rsalakhu: Videos of my Deep Learning (4 part) tutorial at the Simons Institute, Berkeley, are also available here:…

  12. The problem with AI hype...

  13. @AgoniGrammi

  14. RT @MalikaBoulkena: #NIPS2016 in numbers. Very impressive !

  15. RT @AgoniGrammi: Rise of #computervision! #CVPR'17 receives a record high of ~2650 submissions! +25% over @cvpr2016 !!! #machinelearning,#…