activity
20152026
most citedBiased Importance Sampling for Deep Neural Network Training

48 citations · 120 across the 25 of their papers we have counts for

collaborators
Showing 2017Show all

6 papers · 1 filter

stat.ML2017

SGAN: An Alternative Training of Generative Adversarial Networks

Tatjana Chavdarova, François Fleuret

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this…

cs.CV2017★ 10 cited

The WILDTRACK Multi-Camera Person Dataset

Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6

People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…

astro-ph.IM2017★ 13 cited

Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter

Stepan Tulyakov, Anton Ivanov, Nicolas Thomas +5

There are many geometric calibration methods for "standard" cameras. These methods, however, cannot be used for the calibration of telescopes with large focal lengths and complex o…

cs.LG2017★ 48 cited

Biased Importance Sampling for Deep Neural Network Training

Angelos Katharopoulos, François Fleuret

Importance sampling has been successfully used to accelerate stochastic optimization in many convex problems. However, the lack of an efficient way to calculate the importance stil…

cs.LG2017

Kronecker Recurrent Units

Cijo Jose, Moustpaha Cisse, Francois Fleuret

Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrence matrix is ill-conditioned. The former increases the…

cs.CV2017

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Pierre Baqué, François Fleuret, Pascal Fua

People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking alg…