Asja Fischer - Google Scholar

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Citations	2313	2186
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Coauteurs

Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARAdresse e-mail validée de umontreal.ca
Christian IgelDepartment of Computer Science, University of CopenhagenAdresse e-mail validée de diku.dk
Stanisław JastrzębskiPostdoctoral Fellow, New York UniversityAdresse e-mail validée de nyu.edu
Nicolas BallasFacebook AI ResearchAdresse e-mail validée de fb.com
Devansh ArpitSenior Research Scientist, SalesforceAdresse e-mail validée de salesforce.com
Jens LehmannProfessor of Computer Science at the University of Bonn & Fraunhofer IAISAdresse e-mail validée de cs.uni-bonn.de
Lukovnikov DenisRuhr University BochumAdresse e-mail validée de cs.uni-bonn.de
Maxinder S. KanwalPhD Student, Stanford UniversityAdresse e-mail validée de stanford.edu
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKAdresse e-mail validée de ed.ac.uk
Zachary KentonUniversity of OxfordAdresse e-mail validée de cs.ox.ac.uk
David Scott KruegerPhD Student, University of MontrealAdresse e-mail validée de umontreal.ca
Emmanuel BengioMcGill UniversityAdresse e-mail validée de mail.mcgill.ca
Tegan MaharajPhD student, MILA (Polytechnique Montreal)Adresse e-mail validée de polymtl.ca
Aaron CourvilleUniversité de MontréalAdresse e-mail validée de umontreal.ca
Thomas MesnardDeepMindAdresse e-mail validée de google.com
Yuhuai(Tony) WuUniversity of TorontoAdresse e-mail validée de cs.toronto.edu
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaAdresse e-mail validée de iro.umontreal.ca
Agustinus KristiadiUniversity of TübingenAdresse e-mail validée de uni-tuebingen.de
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität BochumAdresse e-mail validée de ini.rub.de
Dong-Hyun LeeAITRICSAdresse e-mail validée de aitrics.com

Asja Fischer

Professor for Machine Learning, Ruhr University Bochum

Adresse e-mail validée de ini.rub.de

machine learning deep learning probabilistic models


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
An introduction to restricted Boltzmann machines A Fischer, C Igel Iberoamerican congress on pattern recognition, 14-36, 2012	486	2012
A closer look at memorization in deep networks D Arpit, S Jastrzębski, N Ballas, D Krueger, E Bengio, MS Kanwal, ... International Conference of Machine Learning (ICML), 233--242, 2017	394	2017
Training restricted Boltzmann machines: An introduction A Fischer, C Igel Pattern Recognition 47 (1), 25-39, 2014	393	2014
Three factors influencing minima in sgd S Jastrzębski, Z Kenton, D Arpit, N Ballas, A Fischer, Y Bengio, A Storkey International Conference of Artificial Neural Networks (ICANN 2018)/ arXiv …, 2017	156	2017
Difference target propagation DH Lee, S Zhang, A Fischer, Y Bengio Joint european conference on machine learning and knowledge discovery in …, 2015	142	2015
Neural network-based question answering over knowledge graphs on word and character level D Lukovnikov, A Fischer, J Lehmann, S Auer Proceedings of the 26th international conference on World Wide Web, 1211-1220, 2017	122	2017
On the regularization of Wasserstein GANs H Petzka, A Fischer, D Lukovnikov International Conference on Learning Representations (ICLR), 2018	89	2018
Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines A Fischer, C Igel International conference on artificial neural networks, 208-217, 2010	71	2010
STDP-Compatible Approximation of Back-Propagation in an Energy-Based Model Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu Neural Computation, 2017	54	2017
Bounding the bias of contrastive divergence learning A Fischer, C Igel Neural computation 23 (3), 664-673, 2011	42	2011
STDP as presynaptic activity times rate of change of postsynaptic activity Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu arXiv preprint arXiv:1509.05936, 2015	41*	2015
Deep Nets Don't Learn via Memorization D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ... International Conference of Learning Representations (ICLR) - workshop track, 2017	35	2017
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length S Jastrzębski, Z Kenton, N Ballas, A Fischer, Y Bengio, A Storkey International Conference on Learning Representations (ICLR) 2019, 2019	29*	2019
How to center deep Boltzmann machines J Melchior, A Fischer, L Wiskott Journal of Machine Learning Research 17 (99), 1-61, 2016	29*	2016
Bidirectional Helmholtz machines J Bornschein, S Shabanian, A Fischer, Y Bengio International Conference on Machine Learning, 2511-2519, 2016	28*	2016
Early Inference in Energy-Based Models Approximates Back-Propagation Y Bengio, A Fischer arXiv preprint arXiv:1510.02777, 2015	24	2015
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer International Semantic Web Conference. Springer, 2019	21	2019
The flip-the-state transition operator for restricted Boltzmann machines K Brügge, A Fischer, C Igel Machine Learning 93 (1), 53-69, 2013	20	2013
Training restricted boltzmann machines A Fischer KI-Künstliche Intelligenz 29 (4), 441-444, 2015	13	2015
Improving Response Selection in Multi-turn Dialogue Systems by Incorporating Domain Knowledge D Chaudhuri, A Kristiadi, J Lehmann, A Fischer SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2018	12*	2018

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