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ORCIDAnahid N. Jalali
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- affiliation: Austrian Institute of Technology, Vienna, Austria
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Conference and Workshop Papers
- 2023
[c5]Anita Graser, Anahid N. Jalali, Jasmin Lampert, Axel Weißenfeld, Krzysztof Janowicz:
Deep Learning From Trajectory Data: a Review of Deep Neural Networks and the Trajectory Data Representations to Train Them. EDBT/ICDT Workshops 2023- 2021
- [c4]Clemens Heistracher, Anahid N. Jalali, Indu Strobl, Axel Suendermann, Sebastian Meixner, Stephanie Holly, Daniel Schall, Bernhard Haslhofer, Jana Kemnitz:
Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data. IECON 2021: 1-6 - 2020
- [c3]Carmina Coronel, Christoph Wiesmeyr, Heinrich Garn, Bernhard Kohn, Anahid Naghibzadeh-Jalali, Alexander Schindler, Markus Wimmer, Magdalena Mandl, Martin Glos, Thomas Penzel, Gerhard Kloesch, Andrijana Stefanic-Kejik, Marion Böck, Eugenijus Kaniusas, Stefan Seidel:
Comparison of PSG signals and Respiratory Movement Signal via 3D Camera in Detecting Sleep Respiratory Events by LSTM Models. APSIPA 2020: 919-923 - [c2]Alexander Schindler, Andrew Lindley, Anahid N. Jalali, Martin Boyer
, Sergiu Gordea, Ross King:
Multi-modal video forensic platform for investigating post-terrorist attack scenarios. MMSys 2020: 365-368 - 2019
- [c1]Anahid N. Jalali, Clemens Heistracher, Alexander Schindler, Bernhard Haslhofer, Tanja Nemeth, Robert Glawar, Wilfried Sihn, Peter De Boer:
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning. ICPHM 2019: 1-8
Informal and Other Publications
- 2024
- [i15]Anita Graser, Anahid N. Jalali, Jasmin Lampert, Axel Weißenfeld, Krzysztof Janowicz:
MobilityDL: A Review of Deep Learning From Trajectory Data. CoRR abs/2402.00732 (2024) - 2023
- [i14]Lam Pham, Dat Ngo, Cam Le, Anahid N. Jalali, Alexander Schindler:
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms. CoRR abs/2305.09463 (2023) - [i13]Anahid N. Jalali, Anita Graser, Clemens Heistracher:
Towards eXplainable AI for Mobility Data Science. CoRR abs/2307.08461 (2023) - [i12]Anahid N. Jalali, Bernhard Haslhofer, Simone Kriglstein, Andreas Rauber:
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis. CoRR abs/2309.11987 (2023) - [i11]Anahid N. Jalali, Andreas Rauber, Jasmin Lampert:
Decoding the Unknown: Unveiling Industrial Time Series Classification with Counterfactuals. ERCIM News 2023(134) (2023) - [i10]Anahid N. Jalali, Alexander Schindler, Anita Zolles:
A Pathway to Combat Climate Change with Human-Centred XAI. ERCIM News 2023(134) (2023) - 2022
- [i9]Clemens Heistracher, Anahid N. Jalali, Jürgen Schneeweiss, Klaudia Kovacs, Catherine Laflamme, Bernhard Haslhofer:
Machine Learning Methods for Health-Index Prediction in Coating Chambers. CoRR abs/2205.15145 (2022) - [i8]Lam Pham, Dat Ngo, Anahid N. Jalali, Alexander Schindler:
Low-complexity deep learning frameworks for acoustic scene classification. CoRR abs/2206.06057 (2022) - [i7]Lam Pham, Dusan Salovic, Anahid N. Jalali, Alexander Schindler, Khoa Tran, Hai Canh Vu, Phu X. Nguyen:
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context. CoRR abs/2210.08610 (2022) - [i6]Peter Biegelbauer, Anahid N. Jalali, Sven Schlarb, Michela Vignoli:
Ethical AI: Why and How? ERCIM News 2022(131) (2022) - 2021
- [i5]Lam Pham, Hieu Tang, Anahid N. Jalali, Alexander Schindler, Ross King:
A Low-Compexity Deep Learning Framework For Acoustic Scene Classification. CoRR abs/2106.06838 (2021) - [i4]Clemens Heistracher, Anahid N. Jalali, Axel Suendermann, Sebastian Meixner, Daniel Schall, Bernhard Haslhofer, Jana Kemnitz:
Minimal-Configuration Anomaly Detection for IIoT Sensors. CoRR abs/2110.04049 (2021) - 2020
- [i3]Alexander Schindler, Andrew Lindley, Anahid N. Jalali, Martin Boyer, Sergiu Gordea, Ross King:
Multi-Modal Video Forensic Platform for Investigating Post-Terrorist Attack Scenarios. CoRR abs/2004.01023 (2020) - 2019
- [i2]Anahid N. Jalali, Clemens Heistracher, Alexander Schindler, Bernhard Haslhofer, Tanja Nemeth, Robert Glawar, Wilfried Sihn, Peter De Boer:
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning. CoRR abs/1904.07686 (2019) - [i1]Anahid N. Jalali, Alexander Schindler, Bernhard Haslhofer:
Understandable Deep Neural Networks for Predictive Maintenance in the Manufacturing Industry. ERCIM News 2019(116) (2019)
Coauthor Index
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