184 citations · 455 across the 17 of their papers we have counts for
5 papers · 1 filter
Toward Optimal Run Racing: Application to Deep Learning Calibration
Olivier Bousquet, Sylvain Gelly, Karol Kurach +4
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural arc…
Critical Hyper-Parameters: No Random, No Cry
Olivier Bousquet, Sylvain Gelly, Karol Kurach +2
The selection of hyper-parameters is critical in Deep Learning. Because of the long training time of complex models and the availability of compute resources in the cloud, "one-sho…
Better Text Understanding Through Image-To-Text Transfer
Karol Kurach, Sylvain Gelly, Michal Jastrzebski +4
Generic text embeddings are successfully used in a variety of tasks. However, they are often learnt by capturing the co-occurrence structure from pure text corpora, resulting in li…
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Michele Covell, Nick Johnston, David Minnen +5
We introduce a stop-code tolerant (SCT) approach to training recurrent convolutional neural networks for lossy image compression. Our methods introduce a multi-pass training method…
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Nick Johnston, Damien Vincent, David Minnen +6
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We…