Max Vladymyrov | DeepAI

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∙ 09/11/2023

Uncovering mesa-optimization algorithms in Transformers

Transformers have become the dominant model in deep learning, but the re...

0 Johannes von Oswald, et al. ∙

∙ 01/11/2023

Continual Few-Shot Learning Using HyperTransformers

We focus on the problem of learning without forgetting from multiple tas...

0 Max Vladymyrov, et al. ∙

∙ 01/05/2023

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

Stochastic gradient descent plays a fundamental role in nearly all appli...

0 Mark Sandler, et al. ∙

∙ 12/15/2022

Transformers learn in-context by gradient descent

Transformers have become the state-of-the-art neural network architectur...

0 Johannes von Oswald, et al. ∙

∙ 11/28/2022

Decentralized Learning with Multi-Headed Distillation

Decentralized learning with private data is a central problem in machine...

0 Andrey Zhmoginov, et al. ∙

∙ 03/29/2022

Fine-tuning Image Transformers using Learnable Memory

In this paper we propose augmenting Vision Transformer models with learn...

0 Mark Sandler, et al. ∙

∙ 01/13/2022

GradMax: Growing Neural Networks using Gradient Information

The architecture and the parameters of neural networks are often optimiz...

0 Utku Evci, et al. ∙

∙ 01/11/2022

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

In this work we propose a HyperTransformer, a transformer-based model fo...

17 Andrey Zhmoginov, et al. ∙

∙ 04/10/2021

Meta-Learning Bidirectional Update Rules

In this paper, we introduce a new type of generalized neural network whe...

11 Mark Sandler, et al. ∙

∙ 11/06/2020

Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...

30 Alexander D'Amour, et al. ∙

∙ 06/26/2019

No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

Nonlinear embedding manifold learning methods provide invaluable visual ...

0 Max Vladymyrov, et al. ∙

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