The V--Dem measurement model: Latent variable analysis for cross-national and cross-temporal expert-coded data
In this article the author considers certain special aspects of the Russian counter-drugs legislation as it is applied to food papaver. The issue’s relevance is associated with numerous lawsuits, including high-profile cases, against businessmen involved in the supply and sale of food containing papaver and its products on charges of drug dealing.
Considering the reasons and legal basis for the formation of such law enforcement practice, the author carries out a detailed study of the Russian counter-drugs legislation. Thus, the author examines the Russian Federation Government Resolution dated June 30, 1998 № 681 "On Approval of the narcotic drugs, psychotropic substances and their precursors list subject to control in the Russian Federation" on its compliance with the Convention "On narcotic drugs" of 1961 and the Federal Law the Russian Federation dated January 8, 1998 № 3-FZ "On Narcotic Drugs and Psychotropic Substances." It turned out that at the heart of many criminal cases there is a terminological inexactitude: Russian legislation embeds a different meaning of the "papaver straw" concept compared to its meaning on the international level.
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy.
The main idea of the article is a possibility of creating software which could generate an expert estimation of START program innovation project description stored in a certain structure file. The software algorithm and some features of the software as long as the future of this kind of approach to the problem are also discussed.
Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.
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.