52 research outputs found
Adversarial Variational Embedding for Robust Semi-supervised Learning
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification.Comment: 9 pages, Accepted by Research Track in KDD 201
A community-based geological reconstruction of Antarctic Ice Sheet deglaciation since the Last Glacial Maximum
A robust understanding of Antarctic Ice Sheet deglacial history since the Last Glacial Maximum is important in order to constrain ice sheet and glacial-isostatic adjustment models, and to explore the forcing mechanisms responsible for ice sheet retreat. Such understanding can be derived from a broad range of geological and glaciological datasets and recent decades have seen an upsurge in such data gathering around the continent and Sub-Antarctic islands. Here, we report a new synthesis of those datasets, based on an accompanying series of reviews of the geological data, organised by sector. We present a series of timeslice maps for 20ka, 15ka, 10ka and 5ka, including grounding line position and ice sheet thickness changes, along with a clear assessment of levels of confidence. The reconstruction shows that the Antarctic Ice sheet did not everywhere reach the continental shelf edge at its maximum, that initial retreat was asynchronous, and that the spatial pattern of deglaciation was highly variable, particularly on the inner shelf. The deglacial reconstruction is consistent with a moderate overall excess ice volume and with a relatively small Antarctic contribution to meltwater pulse 1a. We discuss key areas of uncertainty both around the continent and by time interval, and we highlight potential priorit. © 2014 The Authors
Automatic question answering for multiple stakeholders, the epidemic question answering dataset
Measurement(s) Text Technology Type(s) Natural Languag
Minimal sufficient statistics in location-scale parameter models
Let f be a probability density on the real line, let n be any positive integer, and assume the condition (R) that log f is locally integrable with respect to Lebesgue measure. The either log f is almost everywhere equal to a polynomial of degree less than n, or the order statistic of n independent and identically distributed observations from the location-scale parameter model generated by f is minimal sufficient. It follows, subject to (R) and n#<=#3, that a complete sufficient statistic exists in the normal case only. Also, for f with (R) infinitely divisible but not normal, the order statistic is always minimal sufficient for the corresponding location-scale parameter model. The proof of the main result uses a theorem on the harmonic analysis of translation and dilation invariant function spaces, attributable to K.O. Leland (1968) and L. Schwartz (1947). (orig.)Available from TIB Hannover: RR 9140(99-08) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman
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