The handbook of brain theory and neural networks /

By Michael A. Arbib, Peter Fgldibk, Malcolm P. Young. 2001

Subfields: Model Architectures, Neuroscience

How researchers see this paper

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

In Model Architectures

1. Xiaogang Wang
2. Yoshua Bengio
3. K. Müller
4. Quoc V. Le
5. Daan Wierstra
6. Oriol Vinyals
7. Karen Simonyan
8. Kyunghyun Cho

In Neuroscience

1. Todd S. Braver
2. Gianluca Baldassarre
3. Daniele Caligiore
4. Jeremy R. Reynolds
5. Rafal Bogacz
6. Peilin Zhao
7. K. Müller
8. Peter Carruthers

Citations in the corpus

• Efficient Defenses Against Adversarial Attacks (2017, methodology): “We consider two types of network architectures: simple convolutional neural nets (CNN) [15] and convolutional neural nets with residuals (ResNet) [9].”
• A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “These include DBNs ( Deep Belief Networks ) [10], CNNs ( Convolutional Neural Networks ) [11], RNNs ( Recurrent Neural Networks ) [12] as well as LSTMs ( Long Short-Term Memory ) [13] or AEs ( autoencoders ).”
• Learning to Search with MCTSnets (2018, background): “We have focused on small searches, more similar in scale to the plans that are processed by the human brain (Arbib, 2003), than to the massive-scale searches in high-performance games or planning applications.”