69 citations · 452 across the 35 of their papers we have counts for
3 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…