165 citations · 820 across the 42 of their papers we have counts for
43 papers
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
Haozhe Sun, Isabelle Guyon, Felix Mohr +1
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors. Typically, the last layer…
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Romain Egele, Isabelle Guyon, Yixuan Sun +1
Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be computationally expensive. To reduce costs, Multi-fidelity HPO (MF-HPO) leverages in…
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera +7
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open dat…
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results
Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz +6
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning…
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round
Manh Hung Nguyen, Lisheng Sun, Nathan Grinsztajn +1
Meta-learning from learning curves is an important yet often neglected research area in the Machine Learning community. We introduce a series of Reinforcement Learning-based meta-l…
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design
Gaëtan Serré, Eva Boguslawski, Benjamin Donnot +3
Current rapid changes in climate increase the urgency to change energy production and consumption management, to reduce carbon and other green-house gas production. In this context…