[go: up one dir, main page]

Follow
Max Vladymyrov
Max Vladymyrov
Other namesMaksym Vladymyrov
Anthropic
Verified email at anthropic.com - Homepage
Title
Cited by
Cited by
Year
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
Journal of Machine Learning Research 23 (226), 1-61, 2022
10462022
Transformers learn in-context by gradient descent
J Von Oswald, E Niklasson, E Randazzo, J Sacramento, A Mordvintsev, ...
International Conference on Machine Learning, 35151-35174, 2023
7812023
Hypertransformer: Model generation for supervised and semi-supervised few-shot learning
A Zhmoginov, M Sandler, M Vladymyrov
International Conference on Machine Learning, 27075-27098, 2022
1082022
Gradmax: Growing neural networks using gradient information
U Evci, B van Merrienboer, T Unterthiner, M Vladymyrov, F Pedregosa
arXiv preprint arXiv:2201.05125, 2022
822022
Uncovering mesa-optimization algorithms in transformers
J Von Oswald, M Schlegel, A Meulemans, S Kobayashi, E Niklasson, ...
arXiv preprint arXiv:2309.05858, 2023
792023
Fine-tuning image transformers using learnable memory
M Sandler, A Zhmoginov, M Vladymyrov, A Jackson
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
692022
Entropic affinities: Properties and efficient numerical computation
M Vladymyrov, M Carreira-Perpinan
International conference on machine learning, 477-485, 2013
622013
Locally linear landmarks for large-scale manifold learning
M Vladymyrov, MÁ Carreira-Perpinán
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013
542013
Partial-Hessian strategies for fast learning of nonlinear embeddings
M Vladymyrov, M Carreira-Perpinan
arXiv preprint arXiv:1206.4646, 2012
492012
The variational nystrom method for large-scale spectral problems
M Vladymyrov, M Carreira-Perpinan
International Conference on Machine Learning, 211-220, 2016
352016
Linear-time training of nonlinear low-dimensional embeddings
M Vladymyrov, M Carreira-Perpinan
Artificial Intelligence and Statistics, 968-977, 2014
302014
Linear transformers are versatile in-context learners
M Vladymyrov, J Von Oswald, M Sandler, R Ge
Advances in Neural Information Processing Systems 37, 48784-48809, 2024
272024
Meta-learning bidirectional update rules
M Sandler, M Vladymyrov, A Zhmoginov, N Miller, T Madams, A Jackson, ...
International Conference on Machine Learning, 9288-9300, 2021
192021
A fast, universal algorithm to learn parametric nonlinear embeddings
MA Carreira-Perpinán, M Vladymyrov
Advances in Neural Information Processing Systems 28, 2015
152015
Training trajectories, mini-batch losses and the curious role of the learning rate
M Sandler, A Zhmoginov, M Vladymyrov, N Miller
arXiv preprint arXiv:2301.02312, 2023
132023
How new data permeates LLM knowledge and how to dilute it
C Sun, R Aksitov, A Zhmoginov, NA Miller, M Vladymyrov, U Rueckert, ...
arXiv preprint arXiv:2504.09522, 2025
102025
Learning and unlearning of fabricated knowledge in language models
C Sun, NA Miller, A Zhmoginov, M Vladymyrov, M Sandler
arXiv preprint arXiv:2410.21750, 2024
72024
Continual few-shot learning using hypertransformers
M Vladymyrov, A Zhmoginov, M Sandler
CoRR, 2023
72023
Decentralized learning with multi-headed distillation
A Zhmoginov, M Sandler, N Miller, G Kristiansen, M Vladymyrov
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
72023
No pressure! addressing the problem of local minima in manifold learning algorithms
M Vladymyrov
Advances in neural information processing systems 32, 2019
52019
The system can't perform the operation now. Try again later.
Articles 1–20