According to our database1, Asja Fischer authored at least 19 papers between 2010 and 2018.
Legend:Book In proceedings Article PhD thesis Other
Population-Contrastive-Divergence: Does consistency help with RBM training?
Pattern Recognition Letters, 2018
Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge.
Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018
STDP-Compatible Approximation of Backpropagation in an Energy-Based Model.
Neural Computation, 2017
Graph-based predictable feature analysis.
Machine Learning, 2017
Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level.
Proceedings of the 26th International Conference on World Wide Web, 2017
A Closer Look at Memorization in Deep Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017
How to Center Deep Boltzmann Machines.
Journal of Machine Learning Research, 2016
Bidirectional Helmholtz Machines.
Proceedings of the 33nd International Conference on Machine Learning, 2016
A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines.
Theor. Comput. Sci., 2015
Training Restricted Boltzmann Machines.
Difference Target Propagation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015
Training restricted Boltzmann machines: An introduction.
Pattern Recognition, 2014
The flip-the-state transition operator for restricted Boltzmann machines.
Machine Learning, 2013
Approximation properties of DBNs with binary hidden units and real-valued visible units.
Proceedings of the 30th International Conference on Machine Learning, 2013
An Introduction to Restricted Boltzmann Machines.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2012
Bounding the Bias of Contrastive Divergence Learning.
Neural Computation, 2011
Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives.
Proceedings of the ESANN 2011, 2011
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines.
Proceedings of the Artificial Neural Networks - ICANN 2010, 2010