Auxiliary Guided Autoregressive Variational Autoencoders

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models that encode global image structure into latent variables while autoregressively modeling low level detail. Previous approaches to such hybrid models restrict the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our contribution is a training procedure relying on an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. Our approach can leverage arbitrarily powerful autoregressive decoders, achieves state-of-the art quantitative performance among models with latent variables, and generates qualitatively convincing samples.Comment: Published as a conference paper at ECML-PKDD 201

Globalization, the volatility of intermediate goods prices and economic growth

We set up a dynamic stochastic model of a stylized economy comprising a final output sector (with traditional and modern firms) and an intermediate goods sector. It is shown that market integration reduces the volatility of the rate of return of capital invested in modern firms. The induced portfolio decision of households then leads to reallocation of capital from traditional to modern firms. Despite the presence of a reverse precautionary saving channel, the growth rate unambiguously increases due to the reallocation of capital. Empirical estimates for OECD countries confirm the theoretical resultsglobalization, trade in intermediate goods, portfolio decisions, economic growth

When topic models disagree: keyphrase extraction with mulitple topic models

We explore how the unsupervised extraction of topic-related keywords benefits from combining multiple topic models. We show that averaging multiple topic models, inferred from different corpora, leads to more accurate keyphrases than when using a single topic model and other state-of-the-art techniques. The experiments confirm the intuitive idea that a prerequisite for the significant benefit of combining multiple models is that the models should be sufficiently different, i.e., they should provide distinct contexts in terms of topical word importance

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin