
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
The predictive performance of supervised learning algorithms depends on ...
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Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professionalgrade software applications are powerful but complicatedex...
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Deep Successor Reinforcement Learning
Learning robust value functions given raw observations and rewards is no...
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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Learning goaldirected behavior in environments with sparse feedback is ...
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Nonparametric Spherical Topic Modeling with Word Embeddings
Traditional topic models do not account for semantic regularities in lan...
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The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Ma...
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Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software si...
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JUMPMeans: SmallVariance Asymptotics for Markov Jump Processes
Markov jump processes (MJPs) are used to model a wide range of phenomena...
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Detailed Derivations of SmallVariance Asymptotics for some Hierarchical Bayesian Nonparametric Models
In this note we provide detailed derivations of two versions of smallva...
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Variational Particle Approximations
Approximate inference in highdimensional, discrete probabilistic models...
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Ardavan Saeedi
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