Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Rupsa Saha"'
Autor:
Sondre Glimsdal, Rupsa Saha, Bimal Bhattarai, Charul Giri, Jivitesh Sharma, Svein Anders Tunheim, Rohan Kumar Yadav
Publikováno v:
2022 International Symposium on the Tsetlin Machine (ISTM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f272d7165e65b9d04f8652dbc825299
https://doi.org/10.1109/istm54910.2022.00021
https://doi.org/10.1109/istm54910.2022.00021
Autor:
Rupsa Saha, Sander Jyhne
Publikováno v:
2022 International Symposium on the Tsetlin Machine (ISTM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34874a0d069d75de37ca6c5f05d0b05e
https://doi.org/10.1109/istm54910.2022.00011
https://doi.org/10.1109/istm54910.2022.00011
TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ad69886e0d18fefb01411a1b4c63351
Publikováno v:
Expert systems
e12873
e12873
Tsetlin Machines (TM) use finite state machines for learning and propositional logic to represent patterns. The resulting pattern recognition approach captures information in the form of conjunctive clauses, thus facilitating human interpretation. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68df49329e4151014726a136c3130b17
https://hdl.handle.net/11250/2875379
https://hdl.handle.net/11250/2875379
Publikováno v:
ICISS
Human mistakes in traffic often have terrible consequences. The long-awaited introduction of self-driving vehicles may solve many of the problems with traffic, but much research is still needed before cars are fully autonomous.In this paper, we propo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49f482e1415f964e66ff9b363220cc60
https://doi.org/10.1145/3388176.3388199
https://doi.org/10.1145/3388176.3388199
Publikováno v:
COMAD/CODS
In this paper, we present the demonstration of a system that helps in analytics and visualization of crime information extracted from large text repositories. Extraction of crime indicators is performed using a CNN-BiLSTM based multi-classification n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee832ba7a1b5bce361c31f9af834fbc2
https://doi.org/10.1145/3371158.3371405
https://doi.org/10.1145/3371158.3371405
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030637989
SGAI Conf.
SGAI Conf.
Tsetlin Machines (TMs) are an interpretable pattern recognition approach that captures patterns with high discriminative power from data. Patterns are represented as conjunctive clauses in propositional logic, produced using bandit-learning in the fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f9a78ed9e3a1e564f2276e98270c492
https://doi.org/10.1007/978-3-030-63799-6_5
https://doi.org/10.1007/978-3-030-63799-6_5
Publikováno v:
WI
In this paper, we have proposed natural language processing and deep learning based techniques for the automatic extraction and curation of occupational health and safety related information from safety-related articles. Such articles typically conta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ff942ee740b1876861c66b106d5f1f4
https://doi.org/10.1109/wi.2018.00-56
https://doi.org/10.1109/wi.2018.00-56
Publikováno v:
NLP-TEA@ACL
In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd0769c186bb31bc10f761b132bb60fe
https://doi.org/10.18653/v1/w18-3713
https://doi.org/10.18653/v1/w18-3713
Publikováno v:
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task.
In this paper, we have explored web-based evidence gathering and different linguistic features to automatically extract drug names from tweets and further classify such tweets into Adverse Drug Events or not. We have evaluated our proposed models wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe0785e87ab821c2227ece79b5d07adb
https://doi.org/10.18653/v1/w18-5919
https://doi.org/10.18653/v1/w18-5919