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Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

Feb 10, 2019
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman

* 13 pages 

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Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

Apr 17, 2017
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

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Deep Successor Reinforcement Learning

Jun 08, 2016
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, Samuel J. Gershman

* 10 pages, 6 figures 

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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

May 31, 2016
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum

* 14 pages, 7 figures 

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Nonparametric Spherical Topic Modeling with Word Embeddings

Apr 01, 2016
Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, Sam Gershman

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The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Feb 20, 2016
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams

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Variational Particle Approximations

Dec 06, 2015
Ardavan Saeedi, Tejas D Kulkarni, Vikash Mansinghka, Samuel Gershman

* First two authors contributed equally to this work 

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

Jun 05, 2015
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka

* JMLR: W&CP Volume 37, 2015 pp. 693-701 
* In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) 

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Automatic Inference for Inverting Software Simulators via Probabilistic Programming

May 31, 2015
Ardavan Saeedi, Vlad Firoiu, Vikash Mansinghka

* ICML 2014 AutoML Workshop 

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

Dec 31, 2014
Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson

* 7 pages 

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