no code implementations • ICLR 2019 • Mayank Bansal, Alex Krizhevsky, Abhijit Ogale
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
4 code implementations • 7 Dec 2018 • Mayank Bansal, Alex Krizhevsky, Abhijit Ogale
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
no code implementations • 7 Mar 2016 • Sergey Levine, Peter Pastor, Alex Krizhevsky, Deirdre Quillen
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images.
no code implementations • Journal of Machine Learning Research 2014 • Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
The key idea is to randomly drop units (along with their connections) from the neural network during training.
8 code implementations • 23 Apr 2014 • Alex Krizhevsky
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs.
21 code implementations • NeurIPS 2012 • Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
We trained a large, deep convolutional neural network to classify the 1. 3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes.
Ranked #4 on
Graph Classification
on HIV-fMRI-77
11 code implementations • 3 Jul 2012 • Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data.
Ranked #227 on
Image Classification
on CIFAR-10