Max Vladymyrov

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 22

Top Topics

Manifold Learning

Top Venues

Publications

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. Trans. Mach. Learn. Res. 2024 (2024)
Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge
Linear Transformers are Versatile In-Context Learners. CoRR (2024)
Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
Decentralized Learning with Multi-Headed Distillation. CVPR (2023)
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller
Training trajectories, mini-batch losses and the curious role of the learning rate. CoRR (2023)
Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento
Uncovering mesa-optimization algorithms in Transformers. CoRR (2023)
Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
Continual Few-Shot Learning Using HyperTransformers. CoRR (2023)
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
Transformers Learn In-Context by Gradient Descent. ICML (2023)
Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov
GradMax: Growing Neural Networks using Gradient Information. ICLR (2022)
Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
Decentralized Learning with Multi-Headed Distillation. CoRR (2022)
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
Fine-tuning Image Transformers using Learnable Memory. CoRR (2022)
Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
Transformers learn in-context by gradient descent. CoRR (2022)
Andrey Zhmoginov, Mark Sandler, Max Vladymyrov
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. CoRR (2022)
Utku Evci, Max Vladymyrov, Thomas Unterthiner, Bart van Merriënboer, Fabian Pedregosa
GradMax: Growing Neural Networks using Gradient Information. CoRR (2022)
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
Fine-tuning Image Transformers using Learnable Memory. CVPR (2022)
Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas
Meta-Learning Bidirectional Update Rules. ICML (2021)
Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Agüera y Arcas
Meta-Learning Bidirectional Update Rules. CoRR (2021)
Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Max Vladymyrov
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. CoRR (2019)
Max Vladymyrov
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. NeurIPS (2019)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
Fast, accurate spectral clustering using locally linear landmarks. IJCNN (2017)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
The Variational Nystrom method for large-scale spectral problems. ICML (2016)
Miguel Á. Carreira-Perpiñán, Max Vladymyrov
A fast, universal algorithm to learn parametric nonlinear embeddings. NIPS (2015)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
Linear-time training of nonlinear low-dimensional embeddings. AISTATS (2014)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
Locally Linear Landmarks for Large-Scale Manifold Learning. ECML/PKDD (3) (2013)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
Entropic Affinities: Properties and Efficient Numerical Computation. ICML (3) (2013)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
Partial-Hessian Strategies for Fast Learning of Nonlinear Embeddings CoRR (2012)
Max Vladymyrov, Miguel Á. Carreira-Perpiñán
Fast Training of Nonlinear Embedding Algorithms. ICML (2012)