Pierre Sermanet

Research Scientist in Deep Learning, Computer Vision, Robotics, Self-Supervised Learning @ Google Brain
[LinkedIn] [Scholar] [YouTube] [Twitter] [Email: first dot last at gmail]

Projects  
Time-Contrastive Networks (TCN)
[ Project Page ] [ Paper ] [ BibTex ] [ Video ] [ Dataset ] [ Code ] [ Slides ]
Unsupervised Perceptual Rewards
[ Project Page ] [ Paper ] [ BibTex ] [ Video ] [ Dataset ]
Self-Supervised Imitation Learning
[ Project Page ]
Visual Attention
[ Paper ] [ BibTex ]
Inception / GoogLeNet
[ Code ] [ Paper ] [ BibTex ] [ ImageNet Challenge ]
Dogs vs. Cats Kaggle challenge
[ Leaderboard ]
OverFeat
[ Code ] [ Paper ] [ BibTex ] [ Slides ] [ ImageNet Challenge ] [ Press ]
Pedestrian Detection
[ Video ] [ Paper ] [ BibTex ]
Convolutional Neural Networks Applied to House Numbers Digit Classification
[ Paper ] [ BibTex ]

Traffic Sign Recognition
[ Paper ] [ BibTex ]
Unsupervised Convolutional Feature Hierarchies
[ Paper ] [ BibTex ]
EBLearn
[ Code ] [ Paper ] [ BibTex ]
Teaching NYU Robotics class
[ Class Page ]
LAGR Challenge: Learning Applied to Ground Robots
[ Overview video ] [ Project Page ]
Learning Long-Range Vision for Autonomous Off-Road Driving
[ Paper ] [ BibTex ]
Collision-Free Off-Road Robot Navigation
[ Paper ] [ BibTex ]
Learning Maneuver Dictionaries for Ground Robot Planning
[ Paper ] [ BibTex ]
Mapping and Planning under Uncertainty in Mobile Robots with Long-Range Perception
[ Paper ] [ BibTex ]
Deep Belief Net Learning in a Long-Range Vision System
[ Paper ] [ BibTex ]
Online Learning for Offroad Robots
[ Paper ] [ BibTex ]
EUROBOT Competition
[ Project Page ]