This paper proposes human-interpretable learning of aspect-based sentiment analysis
(ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability …

Convolutional neural networks (CNNs) have obtained astounding successes for important
pattern recognition tasks, but they suffer from high computational complexity and the lack of …

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has
provided competitive pattern classification accuracy in several benchmarks, including text …

Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained
competitive performance in terms of accuracy, memory footprint, energy, and learning speed …

The proliferation of fake news, ie, news intentionally spread for misinformation, poses a
threat to individuals and society. Despite various fact-checking websites such as PolitiFact …

The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct
properties, such as interpretability, simplicity, and hardware-friendliness. Although …

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 …

The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct
properties, including transparent inference and learning using hardware-near building …

Most supervised text classification approaches assume a closed world, counting on all
classes being present in the data at training time. This assumption can lead to unpredictable …

Tsetlin Machines (TM) use finite state machines for learning and propositional logic to
represent patterns. The resulting pattern recognition approach captures information in the …