no code implementations • 26 Oct 2023 • Rupsa Saha, Vladimir I. Zadorozhny, Ole-Christoffer Granmo
We propose a novel way of assessing and fusing noisy dynamic data using a Tsetlin Machine.
no code implementations • 19 Jan 2023 • K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai, Charul Giri, Sondre Glimsdal, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Jivitesh Sharma, Svein Anders Tunheim, Xuan Zhang
This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size.
5 code implementations • 22 Feb 2021 • Rupsa Saha, Ole-Christoffer Granmo, Vladimir I. Zadorozhny, Morten Goodwin
TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns.
2 code implementations • 10 Sep 2020 • K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav
We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.
no code implementations • WS 2018 • Rupsa Saha, Abir Naskar, Tirthankar Dasgupta, Lipika Dey
Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78. 5{\%}, 88{\%} and 82. 9{\%} respectively for Task1 and 33. 2{\%}, 54. 7{\%} and 41. 3{\%} for Task3.
no code implementations • COLING 2018 • Tirthankar Dasgupta, Lipika Dey, Rupsa Saha, Abir Naskar
We have done experiments with a collection of 5000 crime-reporting News articles span over time, and multiple sources.
no code implementations • WS 2018 • Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha
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.
no code implementations • WS 2018 • Tirthankar Dasgupta, Rupsa Saha, Lipika Dey, Abir Naskar
In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text.
no code implementations • WS 2017 • Hardik Meisheri, Rupsa Saha, Priyanka Sinha, Lipika Dey
This paper describes our approach to the Emotion Intensity shared task.
no code implementations • COLING 2016 • Tirthankar Dasgupta, Lipika Dey, Prasenjit Dey, Rupsa Saha
Any real world events or trends that can affect the company{'}s growth trajectory can be considered as risk.