تقرير
Multi-Source Neural Variational Inference
العنوان: | Multi-Source Neural Variational Inference |
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المؤلفون: | Kurle, Richard, Günnemann, Stephan, van der Smagt, Patrick |
سنة النشر: | 2018 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
الوصف: | Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source. This allows us to relate the sources via the shared latent variables by computing divergence measures between individual source's posterior approximations. We explore a variety of options to learn these encoders and to integrate the beliefs they compute into a consistent posterior approximation. We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting. Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence (AAAI) 2019 |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/1811.04451 |
رقم الأكسشن: | edsarx.1811.04451 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |