Ehsan Amid
Ehsan Amid
Senior Research Scientist at Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
Efficiently identifying task groupings for multi-task learning
C Fifty, E Amid, Z Zhao, T Yu, R Anil, C Finn
Advances in Neural Information Processing Systems 34, 27503-27516, 2021
Robust bi-tempered logistic loss based on bregman divergences
E Amid, MK Warmuth, R Anil, T Koren
Advances in Neural Information Processing Systems 32 pre-proceedings …, 2019
TriMap: Large-scale dimensionality reduction using triplets
E Amid, MK Warmuth
arXiv preprint arXiv:1910.00204, 2019
Multiview triplet embedding: Learning attributes in multiple maps
E Amid, A Ukkonen
International Conference on Machine Learning, 1472-1480, 2015
Public data-assisted mirror descent for private model training
E Amid, A Ganesh, R Mathews, S Ramaswamy, S Song, T Steinke, ...
International Conference on Machine Learning, 517-535, 2022
A fast method of steel surface defect detection using decision trees applied to LBP based features
SR Aghdam, E Amid, MF Imani
2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA …, 2012
Reparameterizing Mirror Descent as Gradient Descent
E Amid, MK Warmuth
Advances in Neural Information Processing Systems 33 pre-proceedings …, 2020
Winnowing with Gradient Descent
E Amid, MK Warmuth
Conference on Learning Theory (COLT), 2020
A kernel-learning approach to semi-supervised clustering with relative distance comparisons
E Amid, A Gionis, A Ukkonen
European Conference on Machine Learning and Principles and Practice of …, 2015
Enhanced performance for support vector machines as multiclass classifiers in steel surface defect detection
E Amid, SR Aghdam, H Amindavar
International Journal of Electrical and Computer Engineering 6 (7), 693-697, 2012
Two-temperature logistic regression based on the Tsallis divergence
E Amid, MK Warmuth, S Srinivasan
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Locoprop: Enhancing backprop via local loss optimization
E Amid, R Anil, M Warmuth
International Conference on Artificial Intelligence and Statistics, 9626-9642, 2022
To aggregate or not? learning with separate noisy labels
J Wei, Z Zhu, T Luo, E Amid, A Kumar, Y Liu
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023
Constrained instance and class reweighting for robust learning under label noise
A Kumar, E Amid
arXiv preprint arXiv:2111.05428, 2021
A more globally accurate dimensionality reduction method using triplets
E Amid, MK Warmuth
arXiv preprint arXiv:1803.00854, 2018
Unsupervised feature extraction for multimedia event detection and ranking using audio content
E Amid, A Mesaros, KJ Palomäki, J Laaksonen, M Kurimo
2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014
Exponentiated gradient reweighting for robust training under label noise and beyond
N Majidi, E Amid, H Talebi, MK Warmuth
arXiv preprint arXiv:2104.01493, 2021
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint
E Amid, MK Warmuth
AAAI Conference on Artificial Intelligence, 2020
Learning from randomly initialized neural network features
E Amid, R Anil, W Kotłowski, MK Warmuth
arXiv preprint arXiv:2202.06438, 2022
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