Introduction The Forgotten Age cycle of Arkham Horror is at a close and Fantasy Flight Games already announced the next cycle, The Circle Undone. Not only that, they’ve announced two mythos packs at a rate that… surprised me. A new cycle announcement and two mythos pack announcements in less than two months? Am I the only one who finds the new pace of announcements surprising? Perhaps that means they want to get product out at a faster pace?

On 28 November 2018 I presented a poster at Dutch Demography Day in Utrecht. Here it is:

Detail-Preserving Pooling (DPP) Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and increase the receptive field size. Since pooling by nature is a lossy process, it is crucial that each such layer maintains the portion of the activations that is most important for the network’s discriminability. Yet, simple maximization or averaging over blocks, max or average pooling, or plain downsampling in the form of strided convolutions are the standard. In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning. Inspired by the human visual system, which focuses on local spatial changes, we propose detail-preserving pooling (DPP), an adaptive pooling method that magnifies spatial changes and preserves important structural detail. Importantly, its parameters can be learned jointly with the rest of the network. We analyze some of its theoretical properties and show its empirical benefits on several datasets and networks, where DPP consistently outperforms previous pooling approaches. …

At: Monash University Location: Melbourne, AustraliaWeb: www.monash.eduPosition: Research Fellow (Bioinformatics)

This is the next article in my series on AzureR, a family of packages for working with Azure in R. I’ll give a short introduction on how to use AzureVM to manage Azure virtual machines, and in particular Data Science Virtual Machines (DSVMs).

Our group has done a lot of work with non-standard calling conventions in R.

** Mon 03 December 2018

What is homology?

Smooth Non-Parametric Frontier Analysis (snfa)Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Anal …