International Joint Conference on Neural Networks

By A. Murray. 1993

How researchers see this paper

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

1. Lingxi Xie
2. Liang Zheng
3. Qi Tian
4. Jingdong Wang
5. Yi Yang
6. Yoshua Bengio
7. Stefan Wermter
8. Frank Xing

Citations in the corpus

• A predictive network architecture for a robust and smooth robot docking behavior (2012, background): “A long distance navigation routine [36] is able to navigate the robot into the starting area, but its lack of precision necessitates the docking routine described in this paper.”
• Nurture, Nature, and Caring: We Are Not Prisoners of Our Genes (2002, background): “Leven and Elsberry (1990), for example, developed a neural network model of the negotiation process between two individuals.”
• Natural language based financial forecasting: a survey (2017, background): “However, the temporal properties of the articles are emphasized by employing a combination of recurrent neural network and RBM.”
• Neural networks in business: techniques and applications for the operations researcher (2000, methodology): “Neural networks can also be used as a diagnostic tool, and have been used to detect faults in electrical equipment [91] and satellite communication networks [92].”
• Gradient learning in spiking neural networks by dynamic perturbation of conductances. (2006, background): “In the actor-critic terminology of reinforcement learning [11], one can imagine that the neurons of one brain area (the “actor”) drive actions that are assessed by another brain area (the “critic”), which in response issues a global, scalar reinforcement signal R to the actor (Fig.”
• Missing value imputation on missing completely at random data using multilayer perceptrons (2011, background): “Ssali and Marwala (2008) study the decision trees with neural networks, principal component analysis and genetic algorithms to impute missing data, separating into two architectures, one based on an auto-associative neural network and the other one on the principal component analysis.”
• DisturbLabel: Regularizing CNN on the Loss Layer (2016, background): “Probabilistic Maxout [31] instead turns the Maxout operation [5] to stochastic.”