Andrey Zhmoginov

According to our database, Andrey Zhmoginov authored at least 27 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Contextually Guided Transformers via Low-Rank Adaptation.

Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov, Mark Sandler

CoRR, June, 2025

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones.

Andrey Zhmoginov, Jihwan Lee, Mark Sandler

CoRR, June, 2025

Long Context In-Context Compression by Getting to the Gist of Gisting.

Aleksandar Petrov, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Max Vladymyrov

CoRR, April, 2025

How new data permeates LLM knowledge and how to dilute it.

Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

MELODI: Exploring Memory Compression for Long Contexts.

Yinpeng Chen, DeLesley Hutchins, Aren Jansen, Andrey Zhmoginov, David Racz, Jesper Sparre Andersen

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning.

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

Trans. Mach. Learn. Res., 2024

Learning and Unlearning of Fabricated Knowledge in Language Models.

Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler

CoRR, 2024

Narrowing the Focus: Learned Optimizers for Pretrained Models.

Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov

CoRR, 2024

2023

Continual Few-Shot Learning Using HyperTransformers.

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

CoRR, 2023

Training trajectories, mini-batch losses and the curious role of the learning rate.

Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller

CoRR, 2023

Transformers Learn In-Context by Gradient Descent.

Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov

Proceedings of the International Conference on Machine Learning, 2023

Decentralized Learning with Multi-Headed Distillation.

Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning.

Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov

Proceedings of the International Conference on Machine Learning, 2022

Fine-tuning Image Transformers using Learnable Memory.

Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks.

Andrey Zhmoginov, Dina Bashkirova, Mark Sandler

CoRR, 2021

Meta-Learning Bidirectional Update Rules.

Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Large-Scale Generative Data-Free Distillation.

Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard

CoRR, 2020

Image segmentation via Cellular Automata.

Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agüera y Arcas

CoRR, 2020

Information-Bottleneck Approach to Salient Region Discovery.

Andrey Zhmoginov, Ian Fischer, Mark Sandler

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning.

Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard

Proceedings of the 7th International Conference on Learning Representations, 2019

Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution.

Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

2018

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation.

Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

CoRR, 2018

MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

CycleGAN, a Master of Steganography.

Casey Chu, Andrey Zhmoginov, Mark Sandler

CoRR, 2017

The Power of Sparsity in Convolutional Neural Networks.

Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov

CoRR, 2017

2016

Inverting face embeddings with convolutional neural networks.

Andrey Zhmoginov, Mark Sandler

CoRR, 2016