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 | 1923 | 2017 |
An introduction to restricted Boltzmann machines A Fischer, C Igel Progress in Pattern Recognition, Image Analysis, Computer Vision, and …, 2012 | 870 | 2012 |
Training restricted Boltzmann machines: An introduction A Fischer, C Igel Pattern Recognition 47 (1), 25-39, 2014 | 665 | 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 | 526 | 2017 |
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 | 458 | 2020 |
Difference target propagation DH Lee, S Zhang, A Fischer, Y Bengio Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 389 | 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 | 344 | 2017 |
On the regularization of Wasserstein GANs H Petzka, A Fischer, D Lukovnikov International Conference on Learning Representations (ICLR), 2018 | 283 | 2018 |
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 | 134 | 2021 |
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 | 128 | 2021 |
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 | 120 | 2019 |
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer International Semantic Web Conference. Springer, 2019 | 120 | 2019 |
STDP-Compatible Approximation of Back-Propagation in an Energy-Based Model Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu Neural Computation, 2017 | 113 | 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 | 110* | 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 | 107 | 2019 |
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 | 90 | 2010 |
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 | 77 | 2019 |
Towards the detection of diffusion model deepfakes J Ricker, S Damm, T Holz, A Fischer Proceedings of the 19th International Joint Conference on Computer Vision …, 2024 | 71 | 2024 |
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 | 71 | 2017 |
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 | 69* | 2015 |