69 citations · 452 across the 35 of their papers we have counts for
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cs.LG2017
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…
cs.LG2017★ 29 cited
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…