1 code implementation • 25 Apr 2024 • Ben Williams, Bart van Merriënboer, Vincent Dumoulin, Jenny Hamer, Eleni Triantafillou, Abram B. Fleishman, Matthew McKown, Jill E. Munger, Aaron N. Rice, Ashlee Lillis, Clemency E. White, Catherine A. D. Hobbs, Tries B. Razak, Kate E. Jones, Tom Denton
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments.
no code implementations • 2 Mar 2024 • Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, Nicolas Papernot
In the privacy literature, this is known as membership inference.
1 code implementation • 12 Dec 2023 • Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin
The ability for a machine learning model to cope with differences in training and deployment conditions--e. g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases.
1 code implementation • NeurIPS 2023 • Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, Eleni Triantafillou
This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for `forgetting' and associated metrics for forget quality.
no code implementations • 13 Feb 2023 • Malik Boudiaf, Tom Denton, Bart van Merriënboer, Vincent Dumoulin, Eleni Triantafillou
Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data.
1 code implementation • 14 May 2021 • Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin
Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from new datasets using only a few examples.
no code implementations • 1 Jan 2021 • Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel
In this work, we consider a realistic setting where the relationship between examples can change from episode to episode depending on the task context, which is not given to the learner.
no code implementations • 1 Jan 2021 • Eleni Triantafillou, Vincent Dumoulin, Hugo Larochelle, Richard Zemel
We discover that fine-tuning on episodes of a particular shot can specialize the pre-trained model to solving episodes of that shot at the expense of performance on other shots, in agreement with a trade-off recently observed in the context of end-to-end episodic training.
no code implementations • 10 Dec 2020 • Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge.
no code implementations • 25 Sep 2019 • Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou, Chia-Cheng Liu, Richard Zemel
In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.
13 code implementations • ICLR 2020 • Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
Few-shot classification refers to learning a classifier for new classes given only a few examples.
Ranked #7 on Few-Shot Image Classification on Meta-Dataset Rank
9 code implementations • ICLR 2018 • Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes.
no code implementations • NeurIPS 2017 • Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Few-shot learning refers to understanding new concepts from only a few examples.