Le Cam meets LeCun: Deficiency and Generic Feature Learning

"Deep Learning" methods attempt to learn generic features in an unsupervised fashion from a large unlabelled data set. These generic features should perform as well as the best hand crafted features for any learning problem that makes use of this data. We provide a definition of generic features, characterize when it is possible to learn them and provide methods closely related to the autoencoder and deep belief network of deep learning. In order to do so we use the notion of deficiency and illustrate its value in studying certain general learning problems.Comment: 25 pages, 2 figure

Regional diversity in social perceptions of (ing)

This research was funded by the Leverhulme Trust (grant RPG-215, Erik Schleef PI). We are grateful to all participants in our perception surveys and those students who kindly let us use their voice samples in our experiments. We thank Maciej Baranowski, Miriam Meyerhoff, and Danielle Turton for their expert advice and Ann Houston who kindly granted permission to reproduce her wonderfully illuminating map on the relation of the modern [ɪŋ] ∼ [ɪn] alternation to the distribution of -ing in the 15th century. Michael Ramsammy was involved in the sociolinguistic interview recordings, stimuli and survey creation for Manchester and London. Audiences at the Sixth Northern Englishes Workshop in Lancaster in April 2014 and at the Third Conference of the International Society for the Linguistics of English in Zurich in August 2014 have provided helpful formative feedback. We alone are responsible for any failings in this paper.Peer reviewedPostprin

Information in additional observations of a non-parametric experiment that is not estimable

Given $n$ independent and identically distributed observations and measuring the value of obtaining an additional observation in terms of Le Cam's notion of deficiency between experiments we show for certain types of non-parametric experiments that the value of an additional observation decreases at a rate of $1/\sqrt{n}$. This is distinct from the known typical decrease at a rate of $1/n$ for parametric experiments and non-decreasing value in the case of very large experiments. In particular the rate of $1/\sqrt{n}$ holds for the experiment given by observing samples from a density about which we know only that it is bounded from below by some fixed constant. Thus there exists an experiment where the value of additional observations tends to zero but for which no consistent, in total variation distance, estimator exists.Comment: 29 pages, 3 figure

An ecosystem modeling approach to predicting cod recruitment

Abstract The NORWECOM ocean model system implemented with the ROMS ocean circulation model has been run to simulate conditions over the last 25 years for the North Atlantic. Modelled time series of volume fluxes, primary production and drift of cod larvae through their modelled ambient temperature have been analysed in conjunction with observational based VPA estimated time series of 3-year old cod recruits in the Barents Sea. Individual time series account for less than 50% of the recruitment variability, however a combination of simulated inflow of Atlantic water and primary production accounts for 70% of the variability with a 3-year lead. The prediction indicates an increased recruitment from 2007 to 2008 from about 450 to 700 million individuals with a standard error of near 150 million. Keywords Ecosystem modelling, volume and larval transport, primary production, cod recruitment and prediction, Barents Se