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Unsupervised Learning
ServiceNow Research
Unsupervised Learning
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Pietro Mazzaglia
,
Tim Verbelen
,
Bart Dhoedt
,
Alexandre Lacoste
,
Sai Rajeswar Mudumba
International Conference of Learning Representations (ICLR), 2023.
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Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Pietro Mazzaglia
,
Tim Verbelen
,
Bart Dhoedt
,
Alexandre Lacoste
,
Sai Rajeswar Mudumba
Workshop at the Neural Information Processing Systems (NeurIPS), 2022.
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Overcoming challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
Christopher Beckham
,
Issam H. Laradji
,
Pau Rodriguez
,
David Vazquez
,
Derek Nowrouzezahrai
,
Christopher Pal
Workshop at the Conference on Lifelong Learning Agents (CoLLAs), 2022.
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Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels
Reinforcement learning (RL) aims at autonomously performing complex tasks. To this end, a reward signal is used to steer the learning …
Sai Rajeswar Mudumba
,
Pietro Mazzaglia
,
Tim Verbelen
,
Alexandre Piche
,
Aaron Courville
,
Alexandre Lacoste
Workshop at the International Conference on Machine Learning (ICML), 2022.
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Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use …
Oscar Manas
,
Alexandre Lacoste
,
Xavier Giro-i-Nieto
,
David Vazquez
,
Pau Rodriguez
International Conference on Computer Vision (ICCV), 2021.
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Unsupervised Learning of Dense Visual Representations
Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, …
Pedro O. Pinheiro
,
Amjad Almahairi
,
Ryan Benmalek
,
Florian Golemo
,
Aaron Courville
Conference on Neural Information Processing Systems (NeurIPS), 2020.
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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train …
Hugo Berard
,
Gauthier Gidel
,
Amjad Almahairi
,
Pascal Vincent
,
Simon Lacoste-Julien
International Conference on Learning Representations (ICLR), 2020.
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Neocortical plasticity: an unsupervised cake but no free lunch
The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, …
Eilif Muller
,
Philippe Beaudoin
Workshop at the Neural Information Processing Systems (NeurIPS), 2019.
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On Adversarial Mixup Resynthesis
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We …
Christopher Beckham
,
Sina Honari
,
Vikas Verma
,
Alex Lamb
,
Farnoosh Ghadiri
,
R Devon Hjelm
,
Yoshua Bengio
,
Christopher Pal
Conference on Neural Information Processing Systems (NeurIPS), 2019.
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Reducing Noise in GAN Training with Variance Reduced Extragradient
We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can …
Tatjana Chavdarova
,
Gauthier Gidel
,
François Fleuret
,
Simon Lacoste-Julien
Conference on Neural Information Processing Systems (NeurIPS), 2019.
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