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| Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews, , , and , Idiap-RR-03-2023 |
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| Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews, , , and , in: Proceedings of Interspeech, 2023 |
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| IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach, , , , , , and , Idiap-RR-13-2022 |
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| IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach, , , , , , and , in: The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ EMNLP 2022), 2022 |
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| Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of COVID-19 patients in a UK tertiary hospital, , , , , and , in: BMJ Open, 2022 |
| Online Classifier Adaptation in High Frequency EEG, , and , in: Proceedings of the 3rd International Brain-Computer Interface Workshop & Training Course 2006, 2006 |
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| Towards a Robust BCI: Error Potentials and Online Learning, , and , in: IEEE Trans. on Neural Systems and Rehabilitation Engineering, 14(2), 2006 |
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| Online Classifier Adaptation in Brain-Computer Interfaces, and , Idiap-RR-16-2006 |
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| Integrating Language Identification to improve Multilingual Speech Recognition, , Idiap-RR-24-2012 |
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| Gaussians on Riemannian Manifolds for Robot Learning and Adaptive Control, , in: IEEE Robotics and Automation Magazine (RAM), 2020 |
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| A Tutorial on Task-Parameterized Movement Learning and Retrieval, , in: Intelligent Service Robotics, 9(1):1-29, 2016 |
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| Stochastic learning and control in multiple coordinate systems, , in: Intl Workshop on Human-Friendly Robotics, Genoa, Italy, pages 1-5, 2016 |
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| Robot Learning with Task-Parameterized Generative Models, , in: Proc. Intl Symp. on Robotics Research, 2015 |
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| Skills Learning in Robots by Interaction with Users and Environment, , in: In Proc. of the Intl Conf. on Ubiquitous Robots and Ambient Intelligence (URAI), Kuala Lumpur, Malaysia, pages 161-162, 2014 |
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| Learning from Demonstration (Programming by Demonstration), , in: Encyclopedia of Robotics, Springer, 2019 |
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| Robot Learning with Task-Parameterized Generative Models, , in: Robotics Research, pages 111-126, Springer, 2018 |
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| Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series, , in: Mixture Models and Applications, pages 39-57, Springer, 2019 |
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| A task-parameterized probabilistic model with minimal intervention control, , and , in: Proc. IEEE Intl Conf. on Robotics and Automation (ICRA), Hong Kong, pages 3339 - 3344, IEEE, 2014 |
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| Programming industrial robots from few demonstrations., , in: Human-Robot Collaboration: Unlocking the potential for industrial applications, pages 9-37, Institution of Engineering and Technology (IET), 2023 |
| Learning Control, and , in: Humanoid Robotics: a Reference, pages 1261-1312, Springer, 2019 |
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| The Winning Approach for the Recommendation Systems Shared Task @REST_MEX 2022, , , , and , in: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022), 2022 |
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| Voice-B System, , , , and , in: IEEE 4th Workshop on Intercative Voice Technology for Telecommunications Applications (IVTTA'98) September 29--30, Torino, Italy, 1998 |
| Cursive Character Challenge: a New Database for Machine Learning and Pattern Recognition, , and , in: Proceedings of International Conference on Pattern Recognition (ICPR), 2006 |
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| Cursive Character Challenge: a New Database for Machine Learning and Pattern Recognition, , and , Idiap-RR-79-2005 |
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| Machine Learning for Audio, Image and Video Analysis, and , Springer Verlag, 2008 |
| Combining Neural Gas and Learning Vector Quantization for Cursive Character Recognition, and , in: Neurocomputing, 51, 2003 |
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| Estimating the Intrinsic Dimension of Data with a Fractal-Based Method, and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(10), 2002 |
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| Estimating the Intrinsic Dimension of Data with a Fractal-Based Method, and , Idiap-RR-02-2002 |
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| Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm, and , in: Neural Processing Letters, 14(01), 2001 |
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| Cursive Character Recognition by Learning Vector Quantization, and , in: Pattern Recognition Letters, 22(6), 2001 |
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| Combining Neural Gas and Learning Vector Quantization for Cursive Character Recognition, and , Idiap-RR-18-2001 |
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| Cursive Character Recognition by Learning Vector Quantization, and , Idiap-RR-47-2000 |
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| Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm, and , Idiap-RR-33-2000 |
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| Ambiance in Social Media Venues: Visual Cue Interpretation by Machines and Crowds, , and , in: IEEE CVPR Workshop on Visual Understanding of Subjective Attributes, 2018 |
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| Maya Codical Glyph Segmentation: A Crowdsourcing Approach, , and , in: IEEE Transactions on Multimedia, 20(3):711-725, 2018 |
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| How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs, , and , in: ACM Journal on Computing and Cultural Heritage (JOCCH), 11(4):20, 2018 |
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| Maya Codical Glyph Segmentation: A Crowdsourcing Approach, , and , Idiap-RR-01-2017 |
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| Shape Representations for Maya Codical Glyphs: Knowledge-driven or Deep?, , and , in: 15th International Workshop on Content-Based Multimedia Indexing, 2017 |
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| Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning, , École Polytechnique Fédérale de Lausanne, 2017 |
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| Evaluating Shape Representations for Maya Glyph Classification, , and , in: ACM Journal on Computing and Cultural Heritage (JOCCH), 9(3), 2016 |
| Is That a Jaguar? Segmenting Ancient Maya Glyphs via Crowdsourcing, , and , in: Proc. ACM Int. Workshop on Crowdsourcing for Multimedia, Orlando, pages 37-40, ACM New York, 2014 |
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| Ancient Maya Writings as High-Dimensional Data: a Visualization Approach, , , and , in: Digital Humanities (DH), Krakow, 2016 |
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| Joining high-level symbolic planning with low-level motion primitives in adaptive HRI: application to dressing assistance, , , , and , in: Proc. IEEE Intl Conf. on Robotics and Automation (ICRA), 2018 |
| Object Detection with Active Sample Harvesting, , École Polytechnique Fédérale de Lausanne, 2017 |
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| Large Scale Hard Sample Mining with Monte Carlo Tree Search, and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2016 |
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| Efficient Sample Mining for Object Detection, and , in: Proceedings of the 6th Asian Conference on Machine Learning (ACML), Nha Trang, Vietnam, 2014 |
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| The MuMMER data set for Robot Perception in multi-party HRI Scenarios, , , and , in: Proceedings of the 29th IEEE International Conference on Robot & Human Interactive Communication, 2020 |
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| Importance Sampling Tree for Large-scale Empirical Expectation, , and , in: Proceedings of the International Conference on Machine Learning (ICML), New-York, 2016 |
| Sample Distillation for Object Detection and Image Classification, , and , in: Proceedings of the 6th Asian Conference on Machine Learning (ACML), Nha Trang, Vietnam, 2014 |
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| Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation, , and , in: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, SPAIN, pages 1089-1094, IEEE, 2018 |
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