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Asja Fischer
Asja Fischer
Professor for Machine Learning, Ruhr University Bochum
Zweryfikowany adres z ini.rub.de
Tytuł
Cytowane przez
Cytowane przez
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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
16262017
An introduction to restricted Boltzmann machines
A Fischer, C Igel
Progress in Pattern Recognition, Image Analysis, Computer Vision, and …, 2012
8172012
Training restricted Boltzmann machines: An introduction
A Fischer, C Igel
Pattern Recognition 47 (1), 25-39, 2014
6292014
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
4622017
Difference target propagation
DH Lee, S Zhang, A Fischer, Y Bengio
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015
3572015
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
3272017
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
3212020
On the regularization of Wasserstein GANs
H Petzka, A Fischer, D Lukovnikov
International Conference on Learning Representations (ICLR), 2018
2632018
STDP-Compatible Approximation of Back-Propagation in an Energy-Based Model
Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu
Neural Computation, 2017
1162017
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
1032019
Incorporating literals into knowledge graph embeddings
A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer
International Semantic Web Conference. Springer, 2019
1032019
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 44 (12), 8825 …, 2021
1022021
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
96*2021
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
902019
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
882010
Machine learning in chemical engineering: A perspective
AM Schweidtmann, E Esche, A Fischer, M Kloft, JU Repke, S Sager, ...
Chemie Ingenieur Technik 93 (12), 2029-2039, 2021
812021
Pretrained transformers for simple question answering over knowledge graphs
D Lukovnikov, A Fischer, J Lehmann
The Semantic Web–ISWC 2019: 18th International Semantic Web Conference …, 2019
732019
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
652017
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
57*2015
Bounding the bias of contrastive divergence learning
A Fischer, C Igel
Neural computation 23 (3), 664-673, 2011
542011
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