no code implementations • CVPR 2013 • Thomas Dean, Mark A. Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik
Many object detection systems are constrained by the time required to convolve a target image with a bank of filters that code for different aspects of an object's appearance, such as the presence of component parts.
no code implementations • 17 Nov 2013 • Sinan Yildirim, Sumeetpal Singh, Thomas Dean, Ajay Jasra
We propose sequential Monte Carlo based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas from approximate Bayesian computation.
Computation Methodology
no code implementations • 29 Jun 2018 • Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy Wang, Catherine Wong
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018.