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Andrey Zhmoginov
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2020 – today
- 2024
- [j1]Max Vladymyrov, Andrey Zhmoginov, Mark Sandler:
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. Trans. Mach. Learn. Res. 2024 (2024) - [i21]Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov:
Narrowing the Focus: Learned Optimizers for Pretrained Models. CoRR abs/2408.09310 (2024) - [i20]Yinpeng Chen, DeLesley Hutchins, Aren Jansen, Andrey Zhmoginov, David Racz, Jesper Andersen:
MELODI: Exploring Memory Compression for Long Contexts. CoRR abs/2410.03156 (2024) - [i19]Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler:
Learning and Unlearning of Fabricated Knowledge in Language Models. CoRR abs/2410.21750 (2024) - 2023
- [c10]Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov:
Decentralized Learning with Multi-Headed Distillation. CVPR 2023: 8053-8063 - [c9]Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers Learn In-Context by Gradient Descent. ICML 2023: 35151-35174 - [i18]Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller:
Training trajectories, mini-batch losses and the curious role of the learning rate. CoRR abs/2301.02312 (2023) - [i17]Max Vladymyrov, Andrey Zhmoginov, Mark Sandler:
Continual Few-Shot Learning Using HyperTransformers. CoRR abs/2301.04584 (2023) - 2022
- [c8]Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson:
Fine-tuning Image Transformers using Learnable Memory. CVPR 2022: 12145-12154 - [c7]Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov:
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. ICML 2022: 27075-27098 - [i16]Andrey Zhmoginov, Mark Sandler, Max Vladymyrov:
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. CoRR abs/2201.04182 (2022) - [i15]Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson:
Fine-tuning Image Transformers using Learnable Memory. CoRR abs/2203.15243 (2022) - [i14]Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov:
Decentralized Learning with Multi-Headed Distillation. CoRR abs/2211.15774 (2022) - [i13]Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers learn in-context by gradient descent. CoRR abs/2212.07677 (2022) - 2021
- [c6]Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov, Andrew Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa
, Adriana Kovashka:
BasisNet: Two-Stage Model Synthesis for Efficient Inference. CVPR Workshops 2021: 3081-3090 - [c5]Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas:
Meta-Learning Bidirectional Update Rules. ICML 2021: 9288-9300 - [i12]Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Agüera y Arcas:
Meta-Learning Bidirectional Update Rules. CoRR abs/2104.04657 (2021) - [i11]Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov, Andrew G. Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa, Adriana Kovashka:
BasisNet: Two-stage Model Synthesis for Efficient Inference. CoRR abs/2105.03014 (2021) - [i10]Andrey Zhmoginov, Dina Bashkirova, Mark Sandler:
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks. CoRR abs/2107.10963 (2021) - 2020
- [c4]Andrey Zhmoginov, Ian Fischer, Mark Sandler:
Information-Bottleneck Approach to Salient Region Discovery. ECML/PKDD (3) 2020: 531-546 - [i9]Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agüera y Arcas:
Image segmentation via Cellular Automata. CoRR abs/2008.04965 (2020) - [i8]Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard:
Large-Scale Generative Data-Free Distillation. CoRR abs/2012.05578 (2020)
2010 – 2019
- 2019
- [c3]Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard:
Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution. ICCV Workshops 2019: 1036-1044 - [c2]Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning. ICLR (Poster) 2019 - [i7]Andrey Zhmoginov, Ian Fischer, Mark Sandler:
Information-Bottleneck Approach to Salient Region Discovery. CoRR abs/1907.09578 (2019) - [i6]Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard:
Non-discriminative data or weak model? On the relative importance of data and model resolution. CoRR abs/1909.03205 (2019) - 2018
- [c1]Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen:
MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018: 4510-4520 - [i5]Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen:
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. CoRR abs/1801.04381 (2018) - [i4]Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning. CoRR abs/1810.10703 (2018) - 2017
- [i3]Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov:
The Power of Sparsity in Convolutional Neural Networks. CoRR abs/1702.06257 (2017) - [i2]Casey Chu, Andrey Zhmoginov, Mark Sandler:
CycleGAN, a Master of Steganography. CoRR abs/1712.02950 (2017) - 2016
- [i1]Andrey Zhmoginov, Mark Sandler:
Inverting face embeddings with convolutional neural networks. CoRR abs/1606.04189 (2016)
Coauthor Index
aka: Maksym Vladymyrov
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