169 citations · 253 across the 12 of their papers we have counts for
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stat.ML2019★ 1 cited
Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation
Scott A. Cameron, Hans C. Eggers, Steve Kroon
We consider estimating the marginal likelihood in settings with independent and identically distributed (i.i.d.) data. We propose estimating the predictive distributions in a seque…
stat.ML2019
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Arnu Pretorius, Elan van Biljon, Benjamin van Niekerk +6
Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural network. In this paper, we investigate the effe…