12 citations · 13 across the 4 of their papers we have counts for
4 papers
Neural Coherence : Find higher performance to out-of-distribution tasks from few samples
Simon Guiroy, Mats Richter, Sarath Chandar +1
To create state-of-the-art models for many downstream tasks, it has become common practice to fine-tune a pre-trained large vision model. However, it remains an open question of ho…
Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Simon Guiroy, Christopher Pal, Gonçalo Mordido +1
Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for perform…
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato, Simon Guiroy, Ethan Caballero +2
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs ac…
Towards Understanding Generalization in Gradient-Based Meta-Learning
Simon Guiroy, Vikas Verma, Christopher Pal
In this work we study generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective landscapes. We experimentally demonstrate t…