Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Abeyrathna, K. Darshana"'
Tsetlin Machines (TMs) have emerged as a compelling alternative to conventional deep learning methods, offering notable advantages such as smaller memory footprint, faster inference, fault-tolerant properties, and interpretability. Although various a
Externí odkaz:
http://arxiv.org/abs/2410.17851
Autor:
Abeyrathna, K. Darshana, Abouzeid, Ahmed Abdulrahem Othman, Bhattarai, Bimal, Giri, Charul, Glimsdal, Sondre, Granmo, Ole-Christoffer, Jiao, Lei, Saha, Rupsa, Sharma, Jivitesh, Tunheim, Svein Anders, Zhang, Xuan
Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools ten
Externí odkaz:
http://arxiv.org/abs/2301.08190
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. Although numerous papers explore the TM empirically, many of its proper
Externí odkaz:
http://arxiv.org/abs/2101.02547
Autor:
Abeyrathna, K. Darshana, Bhattarai, Bimal, Goodwin, Morten, Gorji, Saeed, Granmo, Ole-Christoffer, Jiao, Lei, Saha, Rupsa, Yadav, Rohan K.
Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particu
Externí odkaz:
http://arxiv.org/abs/2009.04861
Autor:
Abeyrathna, K. Darshana, Granmo, Ole-Christoffer, Shafik, Rishad, Yakovlev, Alex, Wheeldon, Adrian, Lei, Jie, Goodwin, Morten
Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning tha
Externí odkaz:
http://arxiv.org/abs/2007.02114
Despite significant effort, building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and linear models lack accuracy, while deep learning interpretability is bas
Externí odkaz:
http://arxiv.org/abs/2005.05131
The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in propositional logic to capture the underlying non-linear frequent patterns
Externí odkaz:
http://arxiv.org/abs/2002.01245
The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern cla
Externí odkaz:
http://arxiv.org/abs/1905.04206
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging
Externí odkaz:
http://arxiv.org/abs/1905.04199
Publikováno v:
Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 2020 Feb 01. 378(2164), 1-14.
Externí odkaz:
https://www.jstor.org/stable/26874499
