EBLearn: Open-Source Energy-Based Learning in C++
By Pierre Sermanet, Koray Kavukcuoglu, Yann LeCun. 2009
34 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Optimization & Training
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Mohit Bansal
- Luke S. Zettlemoyer
- Quoc V. Le
- Sergey Levine
- Ruslan Salakhutdinov
- Jianfeng Gao
- Yann LeCun
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Andrea Vedaldi
- Jitendra Malik
- Andrew Zisserman
In Optimization & Training
- Koray Kavukcuoglu
- Yann LeCun
- Pierre Sermanet
- Soumith Chintala
- Luca Benini
- Francesco Conti
- Antonio Pullini
- Clément Farabet
Citations in the corpus
- PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019, background): “While there is a rich offering of open-source software for neural networks in languages other than Python, starting with Lush [14] in Lisp, Torch [6] in C++, Objective-C and Lua, EBLearn [21] in C++, Caffe [1] in C++, the network effects of a large ecosystem such as Python made it an essential skill”
- Convolutional neural networks applied to house numbers digit classification (2012, methodology): “This work was implemented with the EBLearn 1 C++ open-source framework [11].”
- Convolutional networks and applications in vision (2010, background): “The second one is EBlearn, a C++ machine learning library with class hierarchy to the Lush implementation [44].”
- The German Traffic Sign Recognition Benchmark: A multi-class classification competition (2011, methodology): “We use the C++ open-source implementation of ConvNets called EBLearn(6) [17].”
- Traffic sign recognition with multi-scale Convolutional Networks (2011, methodology): “SUMMARY AND FUTURE WORK We presented a Convolutional Network architecture with state-of-the-art results on the GTSRB traffic sign dataset implemented with the EBLearn open-source library [10].”
- Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform (2014, methodology): “For the purpose of this work, we used two tools for network learning: the open source EBLearn [24] tool and a simpler tool (PyConvNet) we developed in Python, based on the open source Theano [5] library.”