This page is meant to give you a quickstart guide to some of the research papers and resources I find most helpful when learning different concepts, ideas, algorithms, and techniques in machine learning and computer vision. I highly recommend reading them if you are doing research in those fields, or just out of morbid curiosity!

Robust Real-time Object Detection

Distinctive Image Features from Scale-Invariant Keypoints

Face Recognition Using Eigenfaces

An Iterative Image Registration Technique with an Application to Stereo Vision

Normalized cuts and image segmentation

Visual Categorization with Bags of Keypoints

ImageNet Classification with Deep Convolutional Neural Networks

Deep Residual Learning for Image Recognition

Improving the Fisher Kernel for Large-Scale Image Classification

Support Vector Networks

Random Forests

Reducing the dimensionality of data using neural networks

Learnability and the Vapnik-Chervonenkis Dimension