Hanna Tseran

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Guido F. MontufarUCLA, Mathematics and Statistics & Data Science, Max Planck Institute MiSVerified email at math.ucla.edu
Tatsuya HaradaThe University of TokyoVerified email at mi.t.u-tokyo.ac.jp
Michael MurrayUniversity of Bath, Mathematical SciencesVerified email at bath.ac.uk
Kedar KarhadkarUCLAVerified email at math.ucla.edu

Hanna Tseran

RIKEN Center for Advanced Intelligence Project (AIP)

Verified email at riken.jp - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Natural variational continual learning H Tseran, ME Khan, T Harada, TD Bui Continual Learning Workshop@ NeurIPS 2, 2018	20	2018
On the expected complexity of maxout networks H Tseran, GF Montufar Advances in Neural Information Processing Systems 34, 28995-29008, 2021	16	2021
Mildly overparameterized relu networks have a favorable loss landscape K Karhadkar, M Murray, H Tseran, G Montúfar arXiv preprint arXiv:2305.19510, 2023	6	2023
Memory augmented neural network with gaussian embeddings for one-shot learning HTT Harada NIPS2017 Workshop on Bayesian Deep Learning, 2017	3	2017
Expected gradients of maxout networks and consequences to parameter initialization H Tseran, G Montúfar International Conference on Machine Learning, 34491-34532, 2023	2	2023

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