Amanuensis: The Programmer's Apprentice

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.

Fast, Accurate Detection of 100,000 Object Classes on a Single Machine

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.

object-detection Object Detection

Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo

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