3D Object Recognition with Deep Belief Nets

By Vinod Nair, Geoffrey E. Hinton. 2009

370 citations recorded by the source corpus.

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Citations in the corpus

High-Performance Neural Networks for Visual Object Classification (2011, neutral): “These results are on par or better than others in the literature: 5.90% error rate for a combination of CNNs and SVMs (LeCun et al. , 2004) and 5.20% error rate for restricted Boltzman machines (Nair and Hinton, 2009).”
Voice conversion in high-order eigen space using deep belief nets (2013, methodology): “DBNs and related models have been used, for example, for hand-written character recognition [21], 3-D object recognition [22], machine transliteration [23], and speech recognition tasks [24].”
Deep Count: Fruit Counting Based on Deep Simulated Learning (2017, unknown): “mean were the”
Deep learning for visual understanding: A review (2016, methodology): “Nair et al. [60] developed a modi ﬁ ed DBN where the top-layer model utilizes a third-order Boltzmann machine for object recognition.”
Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks (2016, unknown): “In application part, deep learning applied in images, computer visions, text, and other characteristics of more complex data to learn[24]-[28][32][33].”
An Analysis of Single-Layer Networks in Unsupervised Feature Learning (2011, unknown): “2%) Deep Belief Networks [18] 95.”
A survey of deep neural network architectures and their applications (2017, unknown): “Breakthroughs on deep learning have been achieved since 2006 when Hinton proposed a novel deep structured learning architecture called deep belief network (DBN) [59].”
Adaptive dropout for training deep neural networks (2013, unknown): “2% third order RBM [12] 6.”