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Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e.g. rotations, reflections, and cropping) play an...
03/30/2021 ∙ by Chen Qiu, et al. ∙
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Variational Dynamic Mixtures
Deep probabilistic time series forecasting models have become an integra...
10/20/2020 ∙ by Chen Qiu, et al. ∙
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Deterministic Inference of Neural Stochastic Differential Equations
Model noise is known to have detrimental effects on neural networks, suc...
06/16/2020 ∙ by Andreas Look, et al. ∙
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Extending Machine Language Models toward Human-Level Language Understanding
Language is central to human intelligence. We review recent breakthrough...
12/12/2019 ∙ by James L. McClelland, et al. ∙
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Structured Embedding Models for Grouped Data
Word embeddings are a powerful approach for analyzing language, and expo...
09/28/2017 ∙ by Maja Rudolph, et al. ∙
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Dynamic Bernoulli Embeddings for Language Evolution
Word embeddings are a powerful approach for unsupervised analysis of lan...
03/23/2017 ∙ by Maja Rudolph, et al. ∙
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Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
10/31/2016 ∙ by Dustin Tran, et al. ∙
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