Google Scholar
Keywords:
all selected optimization learning pattern recognition optimal transport graphics
Preprints
Oops, stay tuned!
Peer-reviewed Papers
CIKM'17
Dual Learning for Cross-domain Image Captioning
Wei Zhao, Wei Xu, Min Yang, Jianbo Ye, Zhou Zhao and Yu Qiao,
ACM International Conference on Information and Knowledge Management, Singapore, November 2017
To appear learning
ICML'17
A Simulated Annealing based Inexact Oracle for Wasserstein Loss Minimization
Jianbo Ye, James Z. Wang and Jia Li,
International Conference on Machine Learning, Sydney, Australia, August 2017
(
arXiv:1608.03859 [stat.CO], August 2016)
pdf & supp
g-scholar
video
optimization learning optimal transport
ACL'17
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering
Jianbo Ye, Yanran Li, Zhaohui Wu, James Z. Wang, Wenjie Li, Jia Li,
Proceedings of The Annual Meeting of the Association for Computational Linguistics,
Vancouver, Canada, July 2017. Long paper.
pdf
g-scholar
code dataset
learning optimal transport
TAFFC
Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data
Jianbo Ye, Jia Li, Michelle G. Newman,
Reginald B. Adams, Jr., James Z. Wang,
IEEE Transactions on Affective Computing, 2017
(
arXiv:1701.01096 [stat.ML], January 2017)
pdf
g-scholar
code dataset
g-scholar
learning
This project also develops a scalable data analytic tool, called accelerated D2-clustering, to process large scale distribution data. It could potentially leverage hundreds of CPUs with a very decent scaling efficiency (say, 70-80%).
If you are a government agency, an education institution, or a non-profit organization, we may offer you a FREE academic license of the C/MPI package to run on clusters.
Please contact authors by email to discuss details. If you are commercial and would like to use our software, let us know and we will try to arrange to let you use.
TSP
Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support
Jianbo Ye, Panruo Wu, James Z. Wang and Jia Li, IEEE Transactions on Signal Processing, January 2017
(
arXiv:1510.00012 [stat.CO], September 2015)
pdf
g-scholar code
optimization learning optimal transport
EAAI'17
Recovering Concept Prerequisite Relations From University Course Dependencies
Chen Liang,
Jianbo Ye, Zhaohui Wu, Bart Pursel, C. Lee Giles,
The AAAI 7th Symposium on Educational Advances in Artifical Intelligence, San Francisco, California, 2017
pdf
g-scholar dataset preprint
learning
ECCV'16
A Distance for HMMs based on Aggregated Wasserstein Metric and State Registration
Yukun Chen,
Jianbo Ye, Jia Li,
European Conference on Computer Vision, Amsterdam, Netherlands, October 2016
Spotlight Presentation
pdf
g-scholar
code
pattern recognition optimal transport
R-BiHDM(-s)[YeYY13], which has been used for SHREC14 human track, is a simple, unsupervised, data independent method. (rank among
the best 3 in scanned human dataset)
IJCV
Shape Retrieval of Non-Rigid 3D Human Models
D. Pickup, et al.,
International Journal on Computer Vision, April 2016, Springer
pdf
g-scholar
pattern recognition graphics
(benchmark paper, earlier version appeared in EG Workshop 3DOR'14)
TVC
A Fast Modal Space Transform For Robust Nonrigid Shape Retrieval
Jianbo Ye and Yizhou Yu,
The Visual Computer, March 2015, Springer
pdf
g-scholar
pattern recognition graphics
(extended version of the ICMR'13 paper)
ICIP'14
Scaling Up Discrete Distribution Clustering Using ADMM
Jianbo Ye and Jia Li,
IEEE International Conference on Image Processing, Paris, France, October 2014
pdf
g-scholar
optimization optimal transport
State-of-the-art unsupervised method to obtain a global nonrigid shape signature for shape retrieval and comparison.
ICMR'13
Fast Nonrigid 3D Retrieval Using Modal Space Transform
Jianbo Ye, Zhicheng Yan, and Yizhou Yu,
ACM International Conference on Multimedia Retrieval, Dallas, April 2013
(Oral, 17.7% acceptance rate)
pdf
g-scholar
project page
dataset
slides
pattern recognition graphics
Working Papers
Yet another model reduction technique for deformable meshes based
on approximation quality controllable subspace.
On the Approximation Theory of Linear Variational Subspace Design
Jianbo Ye and Zhixin Yan,
arXiv:1506.08459 [cs.GR], June 2015
pdf
g-scholar
gitxiv
software
executable demo: linux-x86_64
video (40M)
optimization graphics
Projects
[2015-2016]
AD2-Clustering: a parallel clustering algorithm for discrete distributions, including normalized histogram as a special case, under the Wasserstein metric.
software (to appear)
C MPI
[2014-2015]
neuron: I re-implemented a full-fledged Scala library for composing and training neural network of complex topologies with parameter sharing, supporting different activations, metrics, regularization, and optimization methods. It also includes different variants of multilayer perceptron and auto-encoders.
project page
Scala breeze
[2014-2015]
dmfCramer: Discrete martrix factorizatin with Cramer risk minimization, which is yet another probabilistic matrix factorization method with loss function based on large deviations theory rather than conventional MLE/MAP framework.
project page
Scala breeze
[2013]
Pocket Avatar (intern project at Intel): I developed an efficient data-driven framework for real-time facial expression retargeting.
demo
C++ MATLAB OpenCV
[2012-2013]
iMeshDeform: A C++ mesh deformation framework based on linear variational subspace.
project page
video
C++ MKL PETSc OpenGL
[2011-2012]
R-BiHDM: state-of-the-art, simple and fast signature for nonrigid shapes, which have been tested upon multiple benchmarks.
project page
C++ CGAL OpenGL
