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
	Self-Supervised Actionable Representations [ Project Page ]
	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 ]