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Philip Haeusser
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Flownet: Learning optical flow with convolutional networks
A Dosovitskiy, P Fischer, E Ilg, P Hausser, C Hazirbas, V Golkov, ...
Proceedings of the IEEE international conference on computer vision, 2758-2766, 2015
3273*2015
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N Mayer, E Ilg, P Hausser, P Fischer, D Cremers, A Dosovitskiy, T Brox
Proceedings of the IEEE conference on computer vision and pattern …, 2016
17632016
Associative Domain Adaptation
P Haeusser, T Frerix, A Mordvintsev, D Cremers
In IEEE International Conference on Computer Vision (ICCV), 2017
2232017
Purcell-enhanced single-photon emission from nitrogen-vacancy centers coupled to a tunable microcavity
H Kaupp, T Hümmer, M Mader, B Schlederer, J Benedikter, P Haeusser, ...
Physical Review Applied 6 (5), 054010, 2016
158*2016
Learning by Association - A versatile semi-supervised training method for neural networks
P Häusser, A Mordvintsev, D Cremers
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
1112017
Associative Deep Clustering: Training a Classification Network with no Labels
P Haeusser, J Plapp, V Golkov, E Aljalbout, D Cremers
Proc. of the German Conference on Pattern Recognition (GCPR), 2018
882018
Better text understanding through image-to-text transfer
K Kurach, S Gelly, M Jastrzebski, P Haeusser, O Teytaud, D Vincent, ...
arXiv preprint arXiv:1705.08386, 2017
72017
Golkov,“
A Dosovitskiy, P Fischer, E Ilg, P Häusser, C Hazirbas
FlowNet: Learning Optical Flow with Convolutional Networks,” in ICCV 2, 2015
32015
Learning by association
P Häusser
Technische Universität München, 2018
22018
Functional electrographic flow patterns in patients with persistent atrial fibrillation predict outcome of catheter ablation
T Szili‐Torok, Z Kis, R Bhagwandien, S Wijchers, SC Yap, M Hoogendijk, ...
Journal of Cardiovascular Electrophysiology 32 (8), 2148-2158, 2021
12021
Methods, Systems, Devices, and Components for Visualizing Electrographic Flow (EGF)
DE Luksic, P Haeusser, P Ruppersberg
US Patent App. 16/918,588, 2021
12021
Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient's Heart
P Haeusser, P Ruppersberg
US Patent App. 16/931,844, 2020
12020
Semi-supervised training of neural networks
P Haeusser, A Mordvintsev
US Patent App. 16/461,287, 2020
12020
PO-675-02 USE OF A NEURAL NETWORK TO GENERATE A WHOLE ATRIUM 3D RECONSTRUCTION OF ELECTROGRAPHIC FLOW AND BASKET CATHETER GEOMETRY FROM BIOSIGNALS ALONE
R Gagyi, T Szili-Torok, A Grund, K Ahapov, P Ruppersberg, MH Kong, ...
Heart Rhythm 19 (5), S339-S340, 2022
2022
Electrographic Flow Mapping for Persistent Atrial Fibrillation: Theoretical Basis and Preliminary Observations
D Haines, MH Kong, P Ruppersberg, P Haeusser, B Avitall, T Szili-Torok, ...
2022
Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient's Heart and Generating an Estimate or Probability of …
P Ruppersberg, P Haeusser, MHSM Kong, DE Luksic, KS Ahapov
US Patent App. 17/331,576, 2021
2021
Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient's Heart
P Haeusser, P Ruppersberg
US Patent App. 16/724,254, 2020
2020
Associative Deep Clustering: Training a Classification Network with No Labels
D Cremers
Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany …, 2019
2019
Herstellung und Charakterisierung von silberbeschichteten faserbasierten Fabry-Pérot Mikroresonatoren/Production and Characterization of Silver-Coated Fiber-Based Fabry Pérot …
P Häusser
Ludwig Maximilian Universität München, 2013
2013
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Articles 1–19