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Gabriel Huang
Gabriel Huang
PhD candidate, Mila & Visiting Researcher, ServiceNow
Verified email at umontreal.ca - Homepage
Title
Cited by
Cited by
Year
Negative momentum for improved game dynamics
G Gidel, RA Hemmat, M Pezeshki, R Le Priol, G Huang, S Lacoste-Julien, ...
AISTATS 2019 - Proceedings of the 22nd International Conference on …, 2019
1582019
Scattering networks for hybrid representation learning
E Oyallon, S Zagoruyko, G Huang, N Komodakis, S Lacoste-Julien, ...
IEEE transactions on pattern analysis and machine intelligence 41 (9), 2208-2221, 2018
702018
Multimodal pretraining for dense video captioning
G Huang, B Pang, Z Zhu, C Rivera, R Soricut
AACL-IJCNLP 2020 - Proceedings of the 1st Conference of the Asia-Pacific …, 2020
312020
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Test-Time Labels
G Huang, H Larochelle, S Lacoste-Julien
ICLR 2019 Workshop, 2019
28*2019
A survey of self-supervised and few-shot object detection
G Huang, I Laradji, D Vazquez, S Lacoste-Julien, P Rodriguez
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
212022
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
G Huang, H Berard, A Touati, G Gidel, P Vincent, S Lacoste-Julien
ICLR 2018 Workshop, 2018
202018
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
N Kwon, H Na, G Huang, S Lacoste-Julien
ICLR 2021 - Proceedings of the 9th International Conference on Learning …, 2021
42021
Leveraging Human Preferences to Master Poetry
R Pardinas, G Huang, D Vazquez, A Piché
The AAAI-23 Workshop on Creative AI Across Modalities, 2023
2023
Towards meaningful and data-efficient learning: exploring GAN losses, improving few-shot benchmarks, and multimodal video captioning
G Huang
2022
Parametric Adversarial Divergences are Good Losses for Generative Modeling
G Huang, H Berard, A Touati, G Gidel, P Vincent, S Lacoste-Julien
arXiv preprint arXiv:1708.02511, 2017
2017
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