Ke Sun - RankMe

62 papers A* 12A 1B 2C 1Misc 2Journal 33Unranked 10

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Leveraging Sparse Observations to Predict Species Abundance Across Space and Time. AAAI Md Zahidul Islam, Cameron S. Fletcher, Ke Sun, Amir Dezfouli, Iadine Chades
2025	J	`jnl`	Deterministic Bounds and Random Estimates of Metric Tensors on Neuromanifolds. CoRR Ke Sun
2024	A*	`conf`	Trade-Offs of Diagonal Fisher Information Matrix Estimators. NeurIPS Alexander Soen, Ke Sun
2024	J	`jnl`	Tradeoffs of Diagonal Fisher Information Matrix Estimators. CoRR Alexander Soen, Ke Sun
2023	J	`jnl`	A Note on "Secure Quantized Training for Deep Learning". IACR Cryptol. ePrint Arch. Marcel Keller, Ke Sun
2023	—	`conf`	Non-linear Embeddings in Hilbert Simplex Geometry. TAG-ML Frank Nielsen, Ke Sun
2023	A*	`conf`	Transformed Distribution Matching for Missing Value Imputation. ICML He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla
2023	J	`jnl`	Transformed Distribution Matching for Missing Value Imputation. CoRR He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla
2022	J	`jnl`	Contrastive Laplacian Eigenmaps. CoRR Hao Zhu, Ke Sun, Piotr Koniusz
2022	A*	`conf`	Fair Wrapping for Black-box Predictions. NeurIPS Alexander Soen, Ibrahim M. Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
2022	J	`jnl`	Fair Wrapping for Black-box Predictions. CoRR Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
2022	J	`jnl`	Non-linear Embeddings in Hilbert Simplex Geometry. CoRR Frank Nielsen, Ke Sun
2022	A*	`conf`	Secure Quantized Training for Deep Learning. ICML Marcel Keller, Ke Sun
2022	J	`jnl`	Secure Quantized Training for Deep Learning. IACR Cryptol. ePrint Arch. Marcel Keller, Ke Sun
2021	A*	`conf`	Contrastive Laplacian Eigenmaps. NeurIPS Hao Zhu, Ke Sun, Peter Koniusz
2021	J	`jnl`	High-order Tensor Pooling with Attention for Action Recognition. CoRR Piotr Koniusz, Lei Wang, Ke Sun
2021	A*	`conf`	On the Variance of the Fisher Information for Deep Learning. NeurIPS Alexander Soen, Ke Sun
2021	J	`jnl`	On the Variance of the Fisher Information for Deep Learning. CoRR Alexander Soen, Ke Sun
2021	J	`jnl`	Secure Quantized Training for Deep Learning. CoRR Marcel Keller, Ke Sun
2021	J	`jnl`	q-Neurons: Neuron Activations Based on Stochastic Jackson's Derivative Operators. IEEE Trans. Neural Networks Learn. Syst. Frank Nielsen, Ke Sun
2020	J	`jnl`	Effectiveness of MPC-friendly Softmax Replacement. CoRR Marcel Keller, Ke Sun
2020	J	`jnl`	GraPASA: Parametric graph embedding via siamese architecture. Inf. Sci. Yujun Chen, Ke Sun, Juhua Pu, Zhang Xiong, Xiangliang Zhang
2019	J	`jnl`	A Note on Our Submission to Track 4 of iDASH 2019. CoRR Marcel Keller, Ke Sun
2019	J	`jnl`	A Note on Our Submission to Track 4 of iDASH 2019. IACR Cryptol. ePrint Arch. Marcel Keller, Ke Sun
2019	A	`conf`	Fisher-Bures Adversary Graph Convolutional Networks. UAI Ke Sun, Piotr Koniusz, Zhen Wang
2019	J	`jnl`	Fisher-Bures Adversary Graph Convolutional Networks. CoRR Ke Sun, Piotr Koniusz, Zhen Wang
2019	J	`jnl`	Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions. CoRR Ke Sun, Frank Nielsen
2019	J	`jnl`	Lightlike Neuromanifolds, Occam's Razor and Deep Learning. CoRR Ke Sun, Frank Nielsen
2019	—	`conf`	Non-Euclidean Embeddings for Graph Analytics and Visualisation. SIGGRAPH Asia Posters Daniel Filonik, Tian Feng, Ke Sun, Richard Nock, Alex Collins, Tomasz Bednarz
2018	J	`jnl`	Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures. CoRR Frank Nielsen, Ke Sun
2018	—	`conf`	Guaranteed Deterministic Bounds on the total variation Distance between univariate mixtures. MLSP Frank Nielsen, Ke Sun
2018	J	`jnl`	Intrinsic Universal Measurements of Non-linear Embeddings. CoRR Ke Sun
2018	—	`conf`	Mining top-k Popular Datasets via a Deep Generative Model. IEEE BigData Uchenna Akujuobi, Ke Sun, Xiangliang Zhang
2018	J	`jnl`	On The Chain Rule Optimal Transport Distance. CoRR Frank Nielsen, Ke Sun
2018	A*	`conf`	Representation Learning of Compositional Data. NeurIPS Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
2018	J	`jnl`	q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators. CoRR Frank Nielsen, Ke Sun
2017	J	`jnl`	Clustering in Hilbert simplex geometry. CoRR Frank Nielsen, Ke Sun
2017	J	`jnl`	Coarse Grained Exponential Variational Autoencoders. CoRR Ke Sun, Xiangliang Zhang
2017	Misc	`conf`	Combinatorial bounds on the α-divergence of univariate mixture models. ICASSP Frank Nielsen, Ke Sun
2017	J	`jnl`	On Hölder Projective Divergences. Entropy Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet
2017	J	`jnl`	On Hölder projective divergences. CoRR Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet
2017	A*	`conf`	Relative Fisher Information and Natural Gradient for Learning Large Modular Models. ICML Ke Sun, Frank Nielsen
2017	—	`conf`	k-Means Clustering with Hölder Divergences. GSI Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet
2016	J	`jnl`	Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities. Entropy Frank Nielsen, Ke Sun
2016	J	`jnl`	Guaranteed Bounds on the Kullback-Leibler Divergence of Univariate Mixtures. IEEE Signal Process. Lett. Frank Nielsen, Ke Sun
2016	J	`jnl`	Guaranteed bounds on the Kullback-Leibler divergence of univariate mixtures using piecewise log-sum-exp inequalities. CoRR Frank Nielsen, Ke Sun
2016	J	`jnl`	Relative Natural Gradient for Learning Large Complex Models. CoRR Ke Sun, Frank Nielsen
2015	A*	`conf`	Information Geometry and Minimum Description Length Networks. ICML Ke Sun, Jun Wang, Alexandros Kalousis, Stéphane Marchand-Maillet
2015	—	`—`	Information geometry and data manifold representations. Ke Sun
2015	—	`conf`	Space-Time Local Embeddings. NIPS Ke Sun, Jun Wang, Alexandros Kalousis, Stéphane Marchand-Maillet
2014	A*	`conf`	An Information Geometry of Statistical Manifold Learning. ICML Ke Sun, Stéphane Marchand-Maillet
2014	Misc	`conf`	Efficient two stage decoding scheme to achieve content identification capacity. ICASSP Farzad Farhadzadeh, Ke Sun, Sohrab Fredowsi
2014	C	`conf`	Sparsity on Statistical Simplexes and Diversity in Social Ranking. ACML Ke Sun, Hisham Mohamed, Stéphane Marchand-Maillet
2014	A*	`conf`	Two-Stage Metric Learning. ICML Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis
2014	J	`jnl`	Two-Stage Metric Learning. CoRR Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis
2013	—	`conf`	Learning Representative Nodes in Social Networks. PAKDD (2) Ke Sun, Donn Morrison, Eric Bruno, Stéphane Marchand-Maillet
2012	—	`conf`	Stochastic unfolding. MLSP Ke Sun, Eric Bruno, Stéphane Marchand-Maillet
2012	B	`conf`	Unsupervised skeleton learning for manifold denoising. ICPR Ke Sun, Eric Bruno, Stéphane Marchand-Maillet
2009	—	`conf`	Canonical Dual Approach to Binary Factor Analysis. ICA Ke Sun, Shikui Tu, David Yang Gao, Lei Xu
2008	—	`conf`	Bayesian Ying-Yang Learning on Orthogonal Binary Factor Analysis. ICANN (1) Ke Sun, Lei Xu
2008	J	`jnl`	Mining Weighted Association Rules without Preassigned Weights. IEEE Trans. Knowl. Data Eng. Ke Sun, Fengshan Bai
2005	B	`conf`	A Composite Method to Extract Eye Contour. ACII Ke Sun, Hong Wang