Energy-Based Models in Document Recognition and Computer Vision

62 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning: Methods and Applications (2014, background): “Among the various subclasses of generative or unsupervised deep networks, the energy-based deep models are the most common [28, 20, 213, 268].”
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling (2020, background): “…without reliance on a sampler. made EBMs an appealing candidate for applications in physics (No ´ e et al., 2019), biology (Ingraham et al., 2019), neuroscience (Scellier & Bengio, 2017), and computer vision (LeCun et al., 2007; Osadchy et al., 2007; Xie et al., 2016; 2019; 2018), to name a few.”
An Overview of Deep-Structured Learning for Information Processing (2011, background): “Among the various subclasses of generative deep architecture, the energy-based deep models are the most common (e.g., Ngiam et al., 2011; Bengio, 2009; LeCun et al., 2007).”
Deep Learning and Its Applications to Signal and Information Processing (2011, methodology): “Another interesting work on image recognition with a more general approach than DBN appears in [ 11 ].,Finally, we discuss a very interesting approach of using DBN and deep autoencoder for document indexing and retrieval [ 11 ], [12].”