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Found 7 papers, 4 papers with code
Learning to Drive by Observing the Best and Synthesizing the Worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
The key idea is to randomly drop units (along with their connections) from the neural network during training.
One weird trick for parallelizing convolutional neural networks
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs.
ImageNet Classification with Deep Convolutional Neural Networks
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
Improving neural networks by preventing co-adaptation of feature detectors
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data.
Ranked #205 on
Image Classification
on CIFAR-10
