Lesezeichen (verstecken)615
- The Grid Corpus is a large multitalker audiovisual sentence corpus designed to support joint computational-behavioral studies in speech perception. In brief, the corpus consists of high-quality audio and video (facial) recordings of 1000 sentences spoken by each of 34 talkers (18 male, 16 female), for a total of 34000 sentences. Sentences are of the form "put red at G9 now". audio_25k.zip contains the wav format utterances at a 25 kHz sampling rate in a separate directory per talker alignments.zip provides word-level time alignments, again separated by talker s1.zip, s2.zip etc contain .jpg videos for each talker [note that due to an oversight, no video for talker t21 is available] The Grid Corpus is described in detail in the paper jasagrid.pdf included in the dataset.
- Python implementations of the Boruta all-relevant feature selection method. - GitHub - scikit-learn-contrib/boruta_py: Python implementations of the Boruta all-relevant feature selection method.
Publikationen (verstecken)39
- Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
- Information, 14 (2): 137 (Februar 2023)
- (2016)cite arxiv:1606.04838.
- (2021)cite arxiv:2110.05838Comment: Under review.
- (2014)cite arxiv:1406.7872Comment: Notes prepared to accompany a series of tutorial lectures given by the author at the 1st Lake Michigan Workshop on Combinatorics and Graph Theory, Western Michigan University, March 15--16 2014.
- (2020)cite arxiv:2010.10504Comment: 11 pages, 3 figures, 5 tables. Submitted to NeurIPS SAS 2020 Workshop.
- (2018)cite arxiv:1808.08931Comment: The original version was accepted by KDD2018. Code is publicly available at https://github.com/divelab/dilated.
- (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
- (2014)cite arxiv:1502.05988.
- Frontiers in Neuroscience, (März 2020)
- (2020)
- (2020)cite arxiv:2002.02065Comment: 5 pages.
- (2017)cite arxiv:1704.03976Comment: To be appeared in IEEE Transactions on Pattern Analysis and Machine Intelligence.
- (2019)cite arxiv:1906.02506Comment: NeurIPS 2019.
- (2019)cite arxiv:1911.05722Comment: Technical report.
- (2019)cite arxiv:1911.04252.
- (2019)cite arxiv:1910.06711.
- (2019)cite arxiv:1910.05929Comment: 10 pages, 8 figures.
- (2019)cite arxiv:1909.10893.
- (2019)cite arxiv:1908.04351.
