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Ke Sun 0001
Person information
- affiliation: CSIRO, Data61, Sydney, Australia
- affiliation: Australian National University, Canberra, ACT, Australia
- affiliation (former): King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- affiliation (former): École Polytechnique, France
- affiliation (former): University of Geneva, Switzerland
- affiliation (former): The Chinese University of Hong Kong
- affiliation (former): Tsinghua University, Beijing, China
Other persons with the same name
- Ke Sun — disambiguation page
- Ke Sun 0002 — Carnegie Mellon University, Pittsburgh, PA, USA
- Ke Sun 0003
— Tsinghua University, Beijing, China
- Ke Sun 0004 — Guangxi Normal University, Guilin, China
- Ke Sun 0005 — Baidu, Beijing, China
- Ke Sun 0006
- Ke Sun 0007 — Harbin Institute of Technology, China
- Ke Sun 0008
- Ke Sun 0009
- Ke Sun 0010
- Ke Sun 0011
- Ke Sun 0012
- Ke Sun 0013 — University of Alberta, Department of Mathematical and Statistical Sciences, Edmonton, Canada (and 1 more)
- Ke Sun 0014
- Ke Sun 0015
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2020 – today
- 2024
- [i26]Alexander Soen, Ke Sun:
Tradeoffs of Diagonal Fisher Information Matrix Estimators. CoRR abs/2402.05379 (2024) - 2023
- [c26]He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. ICML 2023: 42159-42186 - [c25]Frank Nielsen, Ke Sun:
Non-linear Embeddings in Hilbert Simplex Geometry. TAG-ML 2023: 254-266 - [i25]He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. CoRR abs/2302.10363 (2023) - [i24]Marcel Keller, Ke Sun:
A Note on "Secure Quantized Training for Deep Learning". IACR Cryptol. ePrint Arch. 2023: 1219 (2023) - 2022
- [c24]Marcel Keller
Secure Quantized Training for Deep Learning. ICML 2022: 10912-10938 - [c23]Alexander Soen, Ibrahim M. Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie:
Fair Wrapping for Black-box Predictions. NeurIPS 2022 - [i23]Hao Zhu, Ke Sun, Piotr Koniusz:
Contrastive Laplacian Eigenmaps. CoRR abs/2201.05493 (2022) - [i22]Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie:
Fair Wrapping for Black-box Predictions. CoRR abs/2201.12947 (2022) - [i21]Frank Nielsen, Ke Sun:
Non-linear Embeddings in Hilbert Simplex Geometry. CoRR abs/2203.11434 (2022) - [i20]Marcel Keller, Ke Sun:
Secure Quantized Training for Deep Learning. IACR Cryptol. ePrint Arch. 2022: 933 (2022) - 2021
- [j6]Frank Nielsen
q-Neurons: Neuron Activations Based on Stochastic Jackson's Derivative Operators. IEEE Trans. Neural Networks Learn. Syst. 32(6): 2782-2789 (2021) - [c22]Hao Zhu, Ke Sun, Peter Koniusz:
Contrastive Laplacian Eigenmaps. NeurIPS 2021: 5682-5695 - [c21]Alexander Soen, Ke Sun:
On the Variance of the Fisher Information for Deep Learning. NeurIPS 2021: 5708-5719 - [i19]Marcel Keller, Ke Sun:
Secure Quantized Training for Deep Learning. CoRR abs/2107.00501 (2021) - [i18]Alexander Soen, Ke Sun:
On the Variance of the Fisher Information for Deep Learning. CoRR abs/2107.04205 (2021) - [i17]Piotr Koniusz, Lei Wang, Ke Sun:
High-order Tensor Pooling with Attention for Action Recognition. CoRR abs/2110.05216 (2021) - 2020
- [j5]Yujun Chen, Ke Sun, Juhua Pu, Zhang Xiong, Xiangliang Zhang
GraPASA: Parametric graph embedding via siamese architecture. Inf. Sci. 512: 1442-1457 (2020) - [i16]Marcel Keller, Ke Sun:
Effectiveness of MPC-friendly Softmax Replacement. CoRR abs/2011.11202 (2020)
2010 – 2019
- 2019
- [c20]Daniel Filonik, Tian Feng, Ke Sun
Non-Euclidean Embeddings for Graph Analytics and Visualisation. SIGGRAPH Asia Posters 2019: 47:1-47:2 - [c19]Ke Sun, Piotr Koniusz, Zhen Wang:
Fisher-Bures Adversary Graph Convolutional Networks. UAI 2019: 465-475 - [i15]Ke Sun, Piotr Koniusz, Zhen Wang:
Fisher-Bures Adversary Graph Convolutional Networks. CoRR abs/1903.04154 (2019) - [i14]Ke Sun, Frank Nielsen:
Lightlike Neuromanifolds, Occam's Razor and Deep Learning. CoRR abs/1905.11027 (2019) - [i13]Marcel Keller, Ke Sun:
A Note on Our Submission to Track 4 of iDASH 2019. CoRR abs/1910.11680 (2019) - [i12]Ke Sun, Frank Nielsen:
Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions. CoRR abs/1911.12463 (2019) - [i11]Marcel Keller, Ke Sun:
A Note on Our Submission to Track 4 of iDASH 2019. IACR Cryptol. ePrint Arch. 2019: 1246 (2019) - 2018
- [c18]Uchenna Akujuobi
Mining top-k Popular Datasets via a Deep Generative Model. IEEE BigData 2018: 584-593 - [c17]Frank Nielsen
Guaranteed Deterministic Bounds on the total variation Distance between univariate mixtures. MLSP 2018: 1-6 - [c16]Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun:
Representation Learning of Compositional Data. NeurIPS 2018: 6680-6690 - [i10]Frank Nielsen, Ke Sun:
q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators. CoRR abs/1806.00149 (2018) - [i9]Frank Nielsen, Ke Sun:
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures. CoRR abs/1806.11311 (2018) - [i8]Ke Sun:
Intrinsic Universal Measurements of Non-linear Embeddings. CoRR abs/1811.01464 (2018) - [i7]Frank Nielsen, Ke Sun:
On The Chain Rule Optimal Transport Distance. CoRR abs/1812.08113 (2018) - 2017
- [j4]Frank Nielsen
On Hölder Projective Divergences. Entropy 19(3): 122 (2017) - [c15]Frank Nielsen
k-Means Clustering with Hölder Divergences. GSI 2017: 856-863 - [c14]Frank Nielsen
Combinatorial bounds on the α-divergence of univariate mixture models. ICASSP 2017: 4476-4480 - [c13]Ke Sun, Frank Nielsen:
Relative Fisher Information and Natural Gradient for Learning Large Modular Models. ICML 2017: 3289-3298 - [i6]Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet:
On Hölder projective divergences. CoRR abs/1701.03916 (2017) - [i5]Ke Sun, Xiangliang Zhang
Coarse Grained Exponential Variational Autoencoders. CoRR abs/1702.07904 (2017) - [i4]Frank Nielsen, Ke Sun:
Clustering in Hilbert simplex geometry. CoRR abs/1704.00454 (2017) - 2016
- [j3]Frank Nielsen
Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities. Entropy 18(12): 442 (2016) - [j2]Frank Nielsen
Guaranteed Bounds on the Kullback-Leibler Divergence of Univariate Mixtures. IEEE Signal Process. Lett. 23(11): 1543-1546 (2016) - [i3]Frank Nielsen, Ke Sun:
Guaranteed bounds on the Kullback-Leibler divergence of univariate mixtures using piecewise log-sum-exp inequalities. CoRR abs/1606.05850 (2016) - [i2]Ke Sun, Frank Nielsen:
Relative Natural Gradient for Learning Large Complex Models. CoRR abs/1606.06069 (2016) - 2015
- [b1]Ke Sun:
Information geometry and data manifold representations. University of Geneva, Switzerland, 2015 - [c12]Ke Sun, Jun Wang, Alexandros Kalousis, Stéphane Marchand-Maillet:
Information Geometry and Minimum Description Length Networks. ICML 2015: 49-58 - [c11]Ke Sun, Jun Wang, Alexandros Kalousis, Stéphane Marchand-Maillet:
Space-Time Local Embeddings. NIPS 2015: 100-108 - 2014
- [c10]Ke Sun, Hisham Mohamed, Stéphane Marchand-Maillet:
Sparsity on Statistical Simplexes and Diversity in Social Ranking. ACML 2014 - [c9]Farzad Farhadzadeh, Ke Sun, Sohrab Fredowsi:
Efficient two stage decoding scheme to achieve content identification capacity. ICASSP 2014: 3814-3818 - [c8]Ke Sun, Stéphane Marchand-Maillet:
An Information Geometry of Statistical Manifold Learning. ICML 2014: 1-9 - [c7]Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis:
Two-Stage Metric Learning. ICML 2014: 370-378 - [i1]Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis:
Two-Stage Metric Learning. CoRR abs/1405.2798 (2014) - 2013
- [c6]Ke Sun, Donn Morrison, Eric Bruno, Stéphane Marchand-Maillet:
Learning Representative Nodes in Social Networks. PAKDD (2) 2013: 25-36 - 2012
- [c5]Ke Sun, Eric Bruno, Stéphane Marchand-Maillet:
Unsupervised skeleton learning for manifold denoising. ICPR 2012: 2719-2722 - [c4]Ke Sun, Eric Bruno, Stéphane Marchand-Maillet:
Stochastic unfolding. MLSP 2012: 1-6
2000 – 2009
- 2009
- [c3]Ke Sun, Shikui Tu, David Yang Gao, Lei Xu:
Canonical Dual Approach to Binary Factor Analysis. ICA 2009: 346-353 - 2008
- [j1]Ke Sun, Fengshan Bai:
Mining Weighted Association Rules without Preassigned Weights. IEEE Trans. Knowl. Data Eng. 20(4): 489-495 (2008) - [c2]Ke Sun, Lei Xu:
Bayesian Ying-Yang Learning on Orthogonal Binary Factor Analysis. ICANN (1) 2008: 255-264 - 2005
- [c1]Ke Sun, Hong Wang:
A Composite Method to Extract Eye Contour. ACII 2005: 112-118
Coauthor Index
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last updated on 2024-12-08 02:25 CET by the dblp team
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