Tam Le
Tam Le
Postdoctoral Researcher at RIKEN AIP
Verified email at iip.ist.i.kyoto-u.ac.jp - Homepage
Title
Cited by
Cited by
Year
Real time traffic sign detection using color and shape-based features
T Le, ST Tran, S Mita, TD Nguyen
Intelligent Information and Database Systems (ACIIDS), 268-278, 2010
612010
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
T Le, M Yamada
Advances in Neural Information Processing Systems (NeurIPS), 2018
332018
Tree-Sliced Variants of Wasserstein Distances
T Le, M Yamada, K Fukumizu, M Cuturi
Advances in Neural Information Processing Systems (NeurIPS), 2019
24*2019
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations
T Le, M Cuturi
International Conference on Machine Learning (ICML), 2002-2011, 2015
152015
Safe Grid Search with Optimal Complexity
E Ndiaye, T Le, OF Takeuchi, J Salmon, I Takeuchi
International Conference on Machine Learning (ICML), 2019
102019
A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data
R Jalem, M Nakayama, Y Noda, T Le, I Takeuchi, Y Tateyama, ...
Science and technology of advanced materials (STAM) 19 (1), 231-242, 2018
102018
Hierarchical spatial matching kernel for image categorization
T Le, Y Kang, A Sugimoto, S Tran, T Nguyen
Image Analysis and Recognition (ICIAR), 141-151, 2011
92011
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
T Le, V Huynh, N Ho, D Phung, M Yamada
arXiv preprint arXiv:1910.04483, 2019
6*2019
Adaptive Euclidean maps for histograms: generalized Aitchison embeddings
T Le, M Cuturi
Machine Learning (MLJ) 99 (2), 169-187, 2015
62015
Generalized Aitchison Embeddings for Histograms
T Le, M Cuturi
Asian Conference on Machine Learning (ACML) 29, 293 - 308, 2013
32013
Flow-based Alignment Approaches for Probability Measures in Different Spaces
T Le*, N Ho*, M Yamada
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
22021
Optimal transport kernels for sequential and parallel neural architecture search
V Nguyen*, T Le*, M Yamada, MA Osborne
arXiv preprint arXiv:2006.07593, 2020
22020
Fast Tree Variants of Gromov-Wasserstein
T Le, N Ho, M Yamada
arXiv preprint arXiv:1910.04462, 2019
22019
Entropy Partial Transport with Tree Metrics: Theory and Practice
T Le*, T Nguyen*
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
12021
Topological Bayesian Optimization with Persistence Diagrams
T Shiraishi, T Le, H Kashima, M Yamada
European Conference on Artificial Intelligence (ECAI), 2020
12020
Computationally efficient tree variants of gromov-wasserstein
T Le, N Ho, M Yamada
arXiv preprint arXiv:1910.04462, 2019
12019
Point-set Distances for Learning Representations of 3D Point Clouds
T Nguyen, QH Pham, T Le, T Pham, N Ho, BS Hua
arXiv preprint arXiv:2102.04014, 2021
2021
Supplementary Material for: Tree-Sliced Variants of Wasserstein Distances
T Le, M Yamada, K Fukumizu, M Cuturi
Advances in Neural Information Processing Systems (NeurIPS), 2019
2019
LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport
Y Liu, M Yamada, YHH Tsai, T Le, R Salakhutdinov, Y Yang
arXiv:1909.02373, 2019
2019
Supplementary Material for: Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
T Le, M Yamada
Advances in Neural Information Processing Systems (NeurIPS), 2018
2018
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