Klaus Greff
Klaus Greff
Machine Learning PhD Student, UniversitÓ della Svizzera Italiana
Verified email at usi.ch - Homepage
Cited by
Cited by
LSTM: A search space odyssey
K Greff, RK Srivastava, J KoutnÝk, BR Steunebrink, J Schmidhuber
IEEE transactions on neural networks and learning systems 28 (10), 2222-2232, 2016
Highway networks
RK Srivastava, K Greff, J Schmidhuber
arXiv preprint arXiv:1505.00387, 2015
Training very deep networks
RK Srivastava, K Greff, J Schmidhuber
arXiv preprint arXiv:1507.06228, 2015
A Clockwork RNN
J Koutnik, K Greff, F Gomez, J Schmidhuber
International Conference on Machine Learning, 1863-1871, 2014
Highway and residual networks learn unrolled iterative estimation
K Greff, RK Srivastava, J Schmidhuber
arXiv preprint arXiv:1612.07771, 2016
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
S Van Steenkiste, M Chang, K Greff, J Schmidhuber
arXiv preprint arXiv:1802.10353, 2018
Multi-Object Representation Learning with Iterative Variational Inference
K Greff, RL Kaufman, R Kabra, N Watters, C Burgess, D Zoran, L Matthey, ...
arXiv preprint arXiv:1903.00450, 2019
Neural Expectation Maximization
K Greff, S van Steenkiste, J Schmidhuber
Advances in Neural Information Processing Systems 30, 6694--6704, 2017
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
J Luketina, M Berglund, K Greff, T Raiko
arXiv preprint, 2015
Tagger: Deep unsupervised perceptual grouping
K Greff, A Rasmus, M Berglund, TH Hao, J Schmidhuber, H Valpola
arXiv preprint arXiv:1606.06724, 2016
The sacred infrastructure for computational research
K Greff, A Klein, M Chovanec, F Hutter, J Schmidhuber
Proceedings of the 16th Python in Science Conference 28, 49-56, 2017
A Comparison between Background Subtraction Algorithms using a Consumer Depth Camera.
K Greff, A BrandŃo, S Krau▀, D Stricker, E Clua
VISAPP (1), 431-436, 2012
Binding via reconstruction clustering
K Greff, RK Srivastava, J Schmidhuber
arXiv preprint arXiv:1511.06418, 2015
Multi-object datasets
R Kabra, C Burgess, L Matthey, RL Kaufman, K Greff, M Reynolds, ...
A perspective on objects and systematic generalization in model-based rl
S van Steenkiste, K Greff, J Schmidhuber
arXiv preprint arXiv:1906.01035, 2019
On the Binding Problem in Artificial Neural Networks
K Greff, S van Steenkiste, J Schmidhuber
arXiv preprint arXiv:2012.05208, 2020
Discovering Boolean Gates in Slime Mould
S Harding, J Koutnik, K Greff, J Schmidhuber, A Adamatzky
Inspired by Nature, 323-337, 2018
Visual steering and verification of mass spectrometry data factorization in air quality research
D Engel, K Greff, C Garth, K Bein, A Wexler, B Hamann, H Hagen
IEEE transactions on visualization and computer graphics 18 (12), 2275-2284, 2012
Segmentation of Data
H Valpola, K Greff
US Patent App. 16/303,333, 2019
Unconventional computing using evolution-in-nanomaterio: neural networks meet nanoparticle networks
K Greff, R van Damme, J Koutnik, H Broersma, J Mikhal, C Lawrence, ...
Eighth International Conference on Future Computational Technologies andá…, 2016
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