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Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
The predictive performance of supervised learning algorithms depends on ...
02/10/2019 ∙ by Ryutaro Tanno, et al. ∙
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Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professional-grade software applications are powerful but complicated-ex...
04/17/2017 ∙ by Ardavan Saeedi, et al. ∙
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Deep Successor Reinforcement Learning
Learning robust value functions given raw observations and rewards is no...
06/08/2016 ∙ by Tejas D. Kulkarni, et al. ∙
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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Learning goal-directed behavior in environments with sparse feedback is ...
04/20/2016 ∙ by Tejas D. Kulkarni, et al. ∙
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Nonparametric Spherical Topic Modeling with Word Embeddings
Traditional topic models do not account for semantic regularities in lan...
04/01/2016 ∙ by Kayhan Batmanghelich, et al. ∙
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The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Ma...
02/20/2016 ∙ by Ardavan Saeedi, et al. ∙
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Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software si...
05/31/2015 ∙ by Ardavan Saeedi, et al. ∙
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JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Markov jump processes (MJPs) are used to model a wide range of phenomena...
03/01/2015 ∙ by Jonathan H. Huggins, et al. ∙
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Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
In this note we provide detailed derivations of two versions of small-va...
12/31/2014 ∙ by Jonathan H. Huggins, et al. ∙
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Variational Particle Approximations
Approximate inference in high-dimensional, discrete probabilistic models...
02/24/2014 ∙ by Ardavan Saeedi, et al. ∙
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