Asja Fischer

Cited by

	All	Since 2016
Citations	2760	2510
h-index	19	19
i10-index	26	25

840

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630

2011201220132014201520162017201820192020202110 15 21 64 109 221 248 454 628 832 121

Co-authors

Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Christian IgelDepartment of Computer Science, University of CopenhagenVerified email at diku.dk
Stanisław JastrzębskiServing as the CSO @ Molecule.one & Assistant Prof. @ Jagiellonian University (GMUM.net)Verified email at nyu.edu
Nicolas BallasFacebook AI ResearchVerified email at fb.com
Jens LehmannProfessor of Computer Science at the University of Bonn & Fraunhofer IAISVerified email at cs.uni-bonn.de
Devansh ArpitSenior Research Scientist, SalesforceVerified email at salesforce.com
Lukovnikov DenisRuhr University BochumVerified email at cs.uni-bonn.de
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Maxinder S. KanwalPhD Student, Stanford UniversityVerified email at stanford.edu
Zachary KentonDeepMindVerified email at google.com
David Scott KruegerPhD Student, University of MontrealVerified email at umontreal.ca
Emmanuel BengioMcGill UniversityVerified email at mail.mcgill.ca
Tegan MaharajPhD student, MILA (Polytechnique Montreal)Verified email at polymtl.ca
Aaron CourvilleUniversité de MontréalVerified email at umontreal.ca
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Thomas MesnardDeepMindVerified email at google.com
Yuhuai(Tony) WuUniversity of TorontoVerified email at cs.toronto.edu
Agustinus KristiadiUniversity of TübingenVerified email at uni-tuebingen.de
Dong-Hyun LeeAITRICSVerified email at aitrics.com
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität BochumVerified email at ini.rub.de

Asja Fischer

Professor for Machine Learning, Ruhr University Bochum

Verified email at ini.rub.de

machine learning deep learning probabilistic models


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
An introduction to restricted Boltzmann machines A Fischer, C Igel Iberoamerican congress on pattern recognition, 14-36, 2012	545	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	523	2017
Training restricted Boltzmann machines: An introduction A Fischer, C Igel Pattern Recognition 47 (1), 25-39, 2014	425	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	188	2017
Difference target propagation DH Lee, S Zhang, A Fischer, Y Bengio Joint european conference on machine learning and knowledge discovery in …, 2015	176	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	159	2017
On the regularization of Wasserstein GANs H Petzka, A Fischer, D Lukovnikov International Conference on Learning Representations (ICLR), 2018	108	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	74	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	63	2017
Bounding the bias of contrastive divergence learning A Fischer, C Igel Neural computation 23 (3), 664-673, 2011	42	2011
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	41	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	37*	2019
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	37*	2015
How to center deep Boltzmann machines J Melchior, A Fischer, L Wiskott Journal of Machine Learning Research 17 (99), 1-61, 2016	32*	2016
Bidirectional Helmholtz machines J Bornschein, S Shabanian, A Fischer, Y Bengio International Conference on Machine Learning, 2511-2519, 2016	30*	2016
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer International Semantic Web Conference. Springer, 2019	29	2019
Early Inference in Energy-Based Models Approximates Back-Propagation Y Bengio, A Fischer arXiv preprint arXiv:1510.02777, 2015	28	2015
The flip-the-state transition operator for restricted Boltzmann machines K Brügge, A Fischer, C Igel Machine Learning 93 (1), 53-69, 2013	21	2013
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs G Maheshwari, P Trivedi, D Lukovnikov, N Chakraborty, A Fischer, ... International Semantic Web Conference. Springer, 2019	20	2019
Introduction to neural network‐based question answering over knowledge graphs N Chakraborty, D Lukovnikov, G Maheshwari, P Trivedi, J Lehmann, ... Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1389, 2021	19*	2021

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