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Łukasz Kidziński
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Dynamic functional principal components
S Hörmann, Ł Kidziński, M Hallin
Journal of the Royal Statistical Society: Series B: Statistical Methodology …, 2015
1732015
MOOC video interaction patterns: What do they tell us?
N Li, Ł Kidziński, P Jermann, P Dillenbourg
Design for Teaching and Learning in a Networked World: 10th European …, 2015
1092015
Deep neural networks enable quantitative movement analysis using single-camera videos
Ł Kidziński, B Yang, JL Hicks, A Rajagopal, SL Delp, MH Schwartz
Nature communications 11 (1), 4054, 2020
952020
Automatic real-time gait event detection in children using deep neural networks
Ł Kidziński, S Delp, M Schwartz
PloS one 14 (1), e0211466, 2019
842019
Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data
LP Prieto, K Sharma, Ł Kidzinski, MJ Rodríguez‐Triana, P Dillenbourg
Journal of computer assisted learning 34 (2), 193-203, 2018
822018
Wearable sensors enable personalized predictions of clinical laboratory measurements
J Dunn, L Kidzinski, R Runge, D Witt, JL Hicks, ...
Nature medicine 27 (6), 1105-1112, 2021
772021
Learning to run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Ł Kidziński, SP Mohanty, CF Ong, Z Huang, S Zhou, A Pechenko, ...
The NIPS'17 Competition: Building Intelligent Systems, 121-153, 2018
772018
Translating head motion into attention-towards processing of student’s body-language
M Raca, L Kidzinski, P Dillenbourg
Proceedings of the 8th international conference on educational data mining, 2015
742015
Automated human-level diagnosis of dysgraphia using a consumer tablet
T Asselborn, T Gargot, Ł Kidziński, W Johal, D Cohen, C Jolly, ...
NPJ digital medicine 1 (1), 42, 2018
702018
Introduction to smart learning analytics: foundations and developments in video-based learning
MN Giannakos, DG Sampson, L Kidziński
682016
How do in-video interactions reflect perceived video difficulty?
N Li, L Kidzinski, P Jermann, P Dillenbourg
Proceedings of the European MOOCs stakeholder summit 2015, 112-121, 2015
622015
Learning to run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
Ł Kidziński, SP Mohanty, CF Ong, JL Hicks, SF Carroll, S Levine, ...
The NIPS'17 Competition: Building Intelligent Systems, 101-120, 2018
572018
Orchestration load indicators and patterns: In-the-wild studies using mobile eye-tracking
LP Prieto, K Sharma, Ł Kidzinski, P Dillenbourg
IEEE Transactions on Learning Technologies 11 (2), 216-229, 2017
512017
How to quantify student’s regularity?
MS Boroujeni, K Sharma, Ł Kidziński, L Lucignano, P Dillenbourg
Adaptive and Adaptable Learning: 11th European Conference on Technology …, 2016
442016
Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation
S Song, Ł Kidziński, XB Peng, C Ong, J Hicks, S Levine, CG Atkeson, ...
Journal of neuroengineering and rehabilitation 18, 1-17, 2021
422021
Automated classification of radiographic knee osteoarthritis severity using deep neural networks
KA Thomas, Ł Kidziński, E Halilaj, SL Fleming, GR Venkataraman, ...
Radiology: Artificial Intelligence 2 (2), e190065, 2020
422020
Semi-Markov Model for Simulating MOOC Students.
L Faucon, L Kidzinski, P Dillenbourg
International Educational Data Mining Society, 2016
342016
Artificial intelligence for prosthetics: Challenge solutions
Ł Kidziński, C Ong, SP Mohanty, J Hicks, S Carroll, B Zhou, H Zeng, ...
The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020
332020
A note on estimation in Hilbertian linear models
S Hörmann, Ł Kidziński
Scandinavian journal of statistics 42 (1), 43-62, 2015
332015
A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis
MA Boswell, SD Uhlrich, K Thomas, JA Kolesar, GE Gold, GS Beaupre, ...
Osteoarthritis and cartilage 29 (3), 346-356, 2021
272021
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