In general, publications from the Computational and Biological Learning Lab and the VLG lab of New York University can be of interest.

Some publications related to EBLearn:

1. Pierre Sermanet, Soumith Chintala and Yann LeCun: Convolutional Neural Networks Applied to House Numbers Digit Classification, ArXiv 2012.
2. Pierre Sermanet and Yann LeCun: Traffic Sign Recognition with Multi-Scale Convolutional Networks, Proceedings of International Joint Conference on Neural Networks (IJCNN'11), 2011
3. Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michaël Mathieu and Yann LeCun: Learning Convolutional Feature Hierachies for Visual Recognition, Advances in Neural Information Processing Systems (NIPS 2010), 2010
4. Pierre Sermanet, Koray Kavukcuoglu and Yann LeCun: EBLearn: Open-Source Energy-Based Learning in C++, Proc. International Conference on Tools with Artificial Intelligence (ICTAI'09), IEEE, 2009
5. Kevin Jarrett, Koray Kavukcuoglu, Marc'Aurelio Ranzato and Yann LeCun: What is the Best Multi-Stage Architecture for Object Recognition?, Proc. International Conference on Computer Vision (ICCV'09), IEEE, 2009
6. Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato and Fu-Jie Huang: Energy-Based Models in Document Recognition and Computer Vision, Proc. International Conference on Document Analysis and Recognition (ICDAR), (keynote address), 2007
7. Yann LeCun, Sumit Chopra, Raia Hadsell, Marc'Aurelio Ranzato and Fu-Jie Huang: A Tutorial on Energy-Based Learning, in Bakir, G. and Hofman, T. and Schölkopf, B. and Smola, A. and Taskar, B. (Eds), Predicting Structured Data, MIT Press, 2006
8. Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998