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Florian Schmid
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2020 – today
- 2024
- [j8]Florian Schmid
, Khaled Koutini
Dynamic Convolutional Neural Networks as Efficient Pre-Trained Audio Models. IEEE ACM Trans. Audio Speech Lang. Process. 32: 2227-2241 (2024) - [i9]Florian Schmid, Paul Primus, Toni Heittola, Annamaria Mesaros, Irene Martín-Morató, Khaled Koutini, Gerhard Widmer:
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge. CoRR abs/2405.10018 (2024) - [i8]Florian Schmid, Paul Primus, Tobias Morocutti, Jonathan Greif, Gerhard Widmer:
Improving Audio Spectrogram Transformers for Sound Event Detection Through Multi-Stage Training. CoRR abs/2408.00791 (2024) - [i7]Paul Primus, Florian Schmid, Gerhard Widmer:
Estimated Audio-Caption Correspondences Improve Language-Based Audio Retrieval. CoRR abs/2408.11641 (2024) - [i6]Florian Schmid, Tobias Morocutti, Francesco Foscarin, Jan Schlüter, Paul Primus, Gerhard Widmer:
Effective Pre-Training of Audio Transformers for Sound Event Detection. CoRR abs/2409.09546 (2024) - 2023
- [c13]Nils Egger
Oral History auf dem Weg zu Big Data: menschliche und maschinelle Annotation lebensgeschichtlicher Interviews im Vergleich. DHd 2023: 257 - [c12]Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer:
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation. EUSIPCO 2023: 176-180 - [c11]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Low-Complexity Audio Embedding Extractors. EUSIPCO 2023: 451-455 - [c10]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Efficient Large-Scale Audio Tagging Via Transformer-to-CNN Knowledge Distillation. ICASSP 2023: 1-5 - [c9]Felix Gerschner
Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data. INDIN 2023: 1-7 - [i5]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Low-Complexity Audio Embedding Extractors. CoRR abs/2303.01879 (2023) - [i4]Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer:
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation. CoRR abs/2305.07499 (2023) - [i3]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models. CoRR abs/2310.15648 (2023) - 2022
- [c8]Florian Schmid, Shahed Masoudian, Khaled Koutini, Gerhard Widmer:
Knowledge Distillation from Transformers for Low-Complexity Acoustic Scene Classification. DCASE 2022 - [c7]Florian Schmid, Jan Schneidewind, Tobias Wild, Stefan Galka, Lukas Schuhegger, Tobias Vogl:
Simulation Based Approach for Reconfiguration and Ramp Up Scenario Analysis in Factory Planning. WSC 2022: 2842-2852 - [i2]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation. CoRR abs/2211.04772 (2022) - [i1]Khaled Koutini, Shahed Masoudian, Florian Schmid, Hamid Eghbal-zadeh, Jan Schlüter, Gerhard Widmer:
Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers. CoRR abs/2211.13956 (2022) - 2021
- [c6]Khaled Koutini, Shahed Masoudian, Florian Schmid, Hamid Eghbal-zadeh, Jan Schlüter, Gerhard Widmer:
Learning General Audio Representations With Large-Scale Training of Patchout Audio Transformers. HEAR@NeurIPS 2021: 65-89
2010 – 2019
- 2019
- [j7]Miguel R. Chuapoco, ManKin Choy, Florian Schmid, Ben A. Duffy, Hyun Joo Lee, Jin Hyung Lee:
Carbon monofilament electrodes for unit recording and functional MRI in same subjects. NeuroImage 186: 806-816 (2019) - [c5]Florian Schmid, Justine Thielemann, Anna Mantwill, Jian Xi, Dirk Labudde, Michael Spranger:
FoSIL - Offensive language classification of German tweets combining SVMs and deep learning techniques. KONVENS 2019 - 2018
- [j6]Ludwig Lausser, Florian Schmid, Lyn-Rouven Schirra, Adalbert F. X. Wilhelm
Rank-based classifiers for extremely high-dimensional gene expression data. Adv. Data Anal. Classif. 12(4): 917-936 (2018) - [j5]Franziska Albers, Florian Schmid, Lydia Wachsmuth, Cornelius Faber
Line scanning fMRI reveals earlier onset of optogenetically evoked BOLD response in rat somatosensory cortex as compared to sensory stimulation. NeuroImage 164: 144-154 (2018) - 2016
- [b1]Florian Schmid:
Robust and interpretable classification models for molecular high-throughput data. University of Ulm, Germany, 2016, pp. 1-120 - [j4]Christoph Müssel, Florian Schmid, Tamara J. Blätte
BiTrinA - multiscale binarization and trinarization with quality analysis. Bioinform. 32(3): 465-468 (2016) - [j3]Florian Schmid, Matthias Schmid
GiANT: gene set uncertainty in enrichment analysis. Bioinform. 32(12): 1891-1894 (2016) - [c4]Lyn-Rouven Schirra, Florian Schmid, Hans A. Kestler
Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorization. ANNPR 2016: 105-116 - 2015
- [j2]Gunnar Völkel, Ludwig Lausser, Florian Schmid, Johann M. Kraus
Sputnik: ad hoc distributed computation. Bioinform. 31(8): 1298-1301 (2015) - 2014
- [j1]Ludwig Lausser, Florian Schmid, Matthias Schmid
Unlabeling data can improve classification accuracy. Pattern Recognit. Lett. 37: 15-23 (2014) - [c3]Florian Schmid, Ludwig Lausser, Hans A. Kestler
Linear Contrast Classifiers in High-Dimensional Spaces. ANNPR 2014: 141-152 - 2012
- [c2]Florian Schmid, Ludwig Lausser, Hans A. Kestler
Three Transductive Set Covering Machines. GfKl 2012: 303-311 - 2011
- [c1]Ludwig Lausser, Florian Schmid, Hans A. Kestler
On the Utility of Partially Labeled Data for Classification of Microarray Data. PSL 2011: 96-109
Coauthor Index
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