Search Results for author: Ardavan Saeedi

Found 12 papers, 4 papers with code

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

1 code implementation CVPR 2019 Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman

We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.

Image Classification

ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations

no code implementations ECCV 2018 Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, Nathan Silberman

We introduce a new method for interpreting computer vision models: visually perceptible, decision-boundary crossing transformations.

Computer Vision

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

no code implementations17 Apr 2017 Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

Professional-grade software applications are powerful but complicated$-$expert users can achieve impressive results, but novices often struggle to complete even basic tasks.

Deep Successor Reinforcement Learning

1 code implementation8 Jun 2016 Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, Samuel J. Gershman

The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards.

Game of Doom reinforcement-learning

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

no code implementations20 Feb 2016 Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams

We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme.

Time Series

Automatic Inference for Inverting Software Simulators via Probabilistic Programming

no code implementations31 May 2015 Ardavan Saeedi, Vlad Firoiu, Vikash Mansinghka

Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions.

Probabilistic Programming

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

no code implementations1 Mar 2015 Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka

We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models.

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

no code implementations31 Dec 2014 Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson

In this note we provide detailed derivations of two versions of small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture models and the HDP hidden Markov model (HDP-HMM, a. k. a.

Variational Particle Approximations

no code implementations24 Feb 2014 Ardavan Saeedi, Tejas D. Kulkarni, Vikash Mansinghka, Samuel Gershman

Like Monte Carlo, DPVI can handle multiple modes, and yields exact results in a well-defined limit.

Spike Sorting Variational Inference

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