Asja Fischer

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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
Jens LehmannProfessor of Computer Science at the University of Bonn & Fraunhofer IAISVerified email at cs.uni-bonn.de
Nicolas BallasFacebook AI ResearchVerified email at fb.com
Stanisław JastrzębskiCTO 👨💻 & CSO 🧪 @ Molecule.OneVerified email at molecule.one
Lukovnikov DenisRuhr University BochumVerified email at rub.de
Devansh ArpitSenior Research Scientist, SalesforceVerified email at salesforce.com
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Zachary KentonDeepMindVerified email at google.com
David Scott KruegerUniversity Assistant Professor, University of CambridgeVerified email at cam.ac.uk
Emmanuel BengioMcGill UniversityVerified email at mail.mcgill.ca
Tegan MaharajAssistant Professor at University of TorontoVerified email at polymtl.ca
Maxinder S. KanwalPhD Student, Stanford UniversityVerified email at stanford.edu
Aaron CourvilleAssociate Professor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified 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) WuGoogle / StanfordVerified email at stanford.edu
Agustinus KristiadiUniversity of TübingenVerified email at uni-tuebingen.de
Dong-Hyun LeeLG AI ResearchVerified email at lgresearch.ai
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


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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	981	2017
An introduction to restricted Boltzmann machines A Fischer, C Igel Iberoamerican congress on pattern recognition, 14-36, 2012	673	2012
Training restricted Boltzmann machines: An introduction A Fischer, C Igel Pattern Recognition 47 (1), 25-39, 2014	544	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	319	2017
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	261	2017
Difference target propagation DH Lee, S Zhang, A Fischer, Y Bengio Joint european conference on machine learning and knowledge discovery in …, 2015	257	2015
On the regularization of Wasserstein GANs H Petzka, A Fischer, D Lukovnikov International Conference on Learning Representations (ICLR), 2018	197	2018
Leveraging Frequency Analysis for Deep Fake Image Recognition J Frank, T Eisenhofer, L Schönherr, A Fischer, D Kolossa, T Holz International Conference of Machine Learning (ICML 2020), 2020	114	2020
STDP-Compatible Approximation of Back-Propagation in an Energy-Based Model Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu Neural Computation, 2017	91	2017
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	81	2010
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	62*	2019
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer International Semantic Web Conference. Springer, 2019	62	2019
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	59	2019
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	58	2017
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 11 (3 …, 2021	56*	2021
Bounding the bias of contrastive divergence learning A Fischer, C Igel Neural computation 23 (3), 664-673, 2011	51	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	50*	2015
How to center deep Boltzmann machines J Melchior, A Fischer, L Wiskott Journal of Machine Learning Research 17 (99), 1-61, 2016	41*	2016
Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework M Ali, M Berrendorf, CT Hoyt, L Vermue, M Galkin, S Sharifzadeh, ... IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021	40	2021
Early Inference in Energy-Based Models Approximates Back-Propagation Y Bengio, A Fischer arXiv preprint arXiv:1510.02777, 2015	34	2015

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