Ardavan Saeedi

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MIT

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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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Variational Particle Approximations

Approximate inference in high-dimensional, discrete probabilistic models...
02/24/2014 ∙ by Ardavan Saeedi, et al. ∙ 0 ∙ share

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Ardavan Saeedi

Featured Co-authors

Joshua B. Tenenbaum

Ryan P. Adams

Karthik Narasimhan

Daniel C. Alexander

Matthew Johnson

Matthew D. Hoffman

Vikash K. Mansinghka

Vikash Mansinghka

Jonathan H. Huggins

Kayhan Batmanghelich

Ryutaro Tanno

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

Deep Successor Reinforcement Learning

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Nonparametric Spherical Topic Modeling with Word Embeddings

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Automatic Inference for Inverting Software Simulators via Probabilistic Programming

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models

Variational Particle Approximations