Deep belief net learning in a long-range vision system for autonomous off-road driving
By Raia Hadsell, Ayse Erkan, Pierre Sermanet, Marco Scoffier, Urs Muller, Yann LeCun. 2008
100 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Jaeyong Sung
- Ashutosh Saxena
- Yoshua Bengio
- Hugo Larochelle
- Seok Hyun Jin
- Sergey Levine
- Andrew Y. Ng
- Hema Swetha Koppula
In Computer Vision and Pattern Recognition
- Yoshua Bengio
- Luc Van Gool
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
- Yi Yang
- Jitendra Malik
- Abhinav Kumar Gupta
Citations in the corpus
- Learning Deep Architectures for AI (2007, methodology): “Since 2006, deep networks have been applied with success not only in classification tasks [2, 17, 99, 111, 150, 153, 195], but also in regression [160], dimensionality reduction [74, 158], modeling textures [141], modeling motion [182, 183], object segmentation [114], information retrieval [154, 159”
- Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds (2015, methodology): “In robotics, deep learning has previously been successfully used for detecting grasps on multi-channel input of RGB-D images [30] and for classifying terrain from long-range vision [16].”
- On the Expressive Power of Deep Architectures (2011, background): “…2009; Mobahi et al., 2009; Osindero and Hinton, 2008), natural language processing (NLP) (Collobert and Weston, 2008a; Weston et al., 2008), robotics (Hadsell et al., 2008), or information retrieval (Salakhutdinov and Hinton, 2007; Salakhutdinov et al., 2007) – deep learning methods significantly…”
- A Survey on Deep Learning (2018, methodology): “used a relatively simple DBN with two convolutional layers and one max subsampling layer to extract deep features [55].”
- Exploring Strategies for Training Deep Neural Networks (2009, background): “…Hinton, 2007a; Collobert and Weston, 2008; Weston et al., 2008), and on different types of problems such as regression (Salakhutdinov and Hinton, 2008), information retrieval (Salakhutdinov and Hinton, 2007a), robotics Hadsell et al. (2008), and collaborative filtering (Salakhutdinov et al., 2007).”
- State-of-the-art review on deep learning in medical imaging. (2019, background): “The most immediate effect can be felt in the field of image processing (2), robotics (3), computer games (4), natural language processing (5), self-driving cars (6) and many others.”