51 citations · 116 across the 15 of their papers we have counts for
Showing 2021 · cs.LGShow all
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cs.LG2021★ 7 cited
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Maria Perez-Ortiz, Omar Rivasplata, Benjamin Guedj +5
Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to l…
cs.LG2021★ 1 cited
Understanding the Distributions of Aggregation Layers in Deep Neural Networks
Eng-Jon Ong, Sameed Husain, Miroslaw Bober
The process of aggregation is ubiquitous in almost all deep nets models. It functions as an important mechanism for consolidating deep features into a more compact representation,…