Andrey Zhmoginov

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Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning
Max Vladymyrov
Andrey Zhmoginov
Mark Sandler
(2024)
Transformers learn in-context by gradient descent
Johannes von Oswald
Eyvind Niklasson
Ettore Randazzo
João Sacramento
Alexander Mordvintsev
Andrey Zhmoginov
Max Vladymyrov
International Conference on Machine Learning (2023), pp. 35151-35174
Fine-Tuning Image Transformers Using Learnable Memory
Andrew Jackson
Andrey Zhmoginov
Mark Sandler
Max Vladymyrov
CVPR 2022
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
Andrey Zhmoginov
Mark Sandler
Max Vladymyrov
ICML (2022)
Meta-Learning Bidirectional Update Rules
Mark Sandler
Max Vladymyrov
Andrey Zhmoginov
Nolan Andrew Miller
Andrew Jackson
Tom Madams
Blaise Hilary Aguera-Arcas
ICML 2021
BasisNet: Two-Stage Model Synthesis for Efficient Inference
Mingda Zhang
Chun-Te Chu
Andrey Zhmoginov
Andrew Howard
Brendan Jou
Yukun Zhu
Li Zhang
Rebecca Hwa
Adriana Kovashka
CVPR Workshop on Efficient Deep Learning for Computer Vision (ECV) (2021)
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks
Andrey Zhmoginov
Dina Bashkirova
Mark Sandler
arXiv (2021)
Information-Bottleneck Approach to Self-Attention
Andrey Zhmoginov
Ian Fischer
Mark Sandler
ICML 2019 Workshop on Self-Supervised learning
Non-discriminative data or weak model? On the relative importance of data and model resolution
Andrew Howard
Andrey Zhmoginov
Jonathan Michael Baccash
Mark Sandler
ICCV (2019)
K for the price of 1. Parameter efficient multi-task and transfer learning
Pramod Kaushik Mudrakarta
Mark Sandler
Andrey Zhmoginov
Andrew Howard
International Conference on Learning Representations (2019)
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