The Deep Weight Prior

Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of trained convolutional filters e.g., spatial correlations of weights. We define DWP in the form of an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that DWP improves the performance of Bayesian neural networks when training data are limited, and initialization of weights with samples from DWP accelerates training of conventional convolutional neural networks.Comment: TL;DR: The deep weight prior learns a generative model for kernels of convolutional neural networks, that acts as a prior distribution while training on new dataset

Les flux migratoires dans l’oblast d’Irkoutsk : orientation et tendances

Cet article analyse les flux migratoires de l’oblast d’Irkoutsk dans le contexte des mobilités nationale et internationale. A l’échelle nationale, l’oblast d’Irkoutsk reçoit des flux de migrants de la République de Bouriatie, de la région de la Transbaïkalie et des régions de l’Extrême-Orient, mais perd aussi des habitants avec des flux d’émigration vers la région de Krasnoïarsk, l’oblast de Novossibirsk et les régions centrales du pays. Pour les migrations internationales, les corridors d’immigration les plus importants proviennent des pays d’Asie Centrale, du Caucase et de la Transcaucasie, ainsi que d’Ukraine. Les flux migratoires d’autres pays sont peu importants. Les migrants originaires de Chine ont augmenté en 2017, mais leur nombre n’est pas comparable aux flux de migrants des pays d’Asie centrale et du Caucase. On constate que la région perd des populations qualifiées en âge de travailler. Les résultats de l’enquête d’opinions de la population à l’égard de la migration, sur la base des données d’enquêtes sociologiques, montrent que l’émigration peut potentiellement prendre une ampleur considérable en raison de la quête de meilleures conditions de vie et de travail hors de l’oblast d’Irkoutsk.This study identifies stable migration flows in Irkutsk district within the national framework of migration flows between different regions of Russian Federation. Some of the flows have been identified as facilitating population growth in Irkutsk Oblast (inflows from Republic of Buryatia, Zabaykalsky Krai, and the Russian Far East regions) and others are identified as working in the opposite direction (outflows to Krasnoyarski Krai, Novosibirsk Oblast, and central Russia regions). The analysis of international migration identifies primarily inflow migration channels from Central Asian countries, Caucasia and Transcaucasia, as well as Ukraine. Migration exchanges with other foreign countries, further afar, are very limited ; migration inflow from China has increased in 2017, but the scope of this inflow remains limited relatively to the migration inflows from neighbouring countries. The results of this study show that Irkutsk district suffers from the loss of skilled working-age population. The results of the 2018 sociological study on the attitude of the Irkutsk district population towards migration show a significant potential for further migration outflows due mostly to a search for a better life and working conditions