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Deployment of artificial intelligence models on edge devices: A tutorial brief
Artificial intelligence (AI) on an edge device has enormous potential, including advanced
signal filtering, event detection, optimization in communications and data compression …
signal filtering, event detection, optimization in communications and data compression …
The convolutional Tsetlin machine
Convolutional neural networks (CNNs) have obtained astounding successes for important
pattern recognition tasks, but they suffer from high computational complexity and the lack of …
pattern recognition tasks, but they suffer from high computational complexity and the lack of …
Explainable tsetlin machine framework for fake news detection with credibility score assessment
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 …
threat to individuals and society. Despite various fact-checking websites such as PolitiFact …
Field data analysis and risk assessment of gas kick during industrial deepwater drilling process based on supervised learning algorithm
During industrial offshore deep-water drilling process, gas kick event occurs frequently due
to extremely narrow Mud Weight (MW) window (minimum 0.01 sg) and negligible safety …
to extremely narrow Mud Weight (MW) window (minimum 0.01 sg) and negligible safety …
Massively parallel and asynchronous tsetlin machine architecture supporting almost constant-time scaling
Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained
competitive performance in terms of accuracy, memory footprint, energy, and learning speed …
competitive performance in terms of accuracy, memory footprint, energy, and learning speed …
Extending the tsetlin machine with integer-weighted clauses for increased interpretability
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 …
many pattern recognition problems. In general, rule-based and linear models lack accuracy …
On the Convergence of Tsetlin Machines for the IDENTITY-and NOT Operators
The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct
properties, such as interpretability, simplicity, and hardware-friendliness. Although …
properties, such as interpretability, simplicity, and hardware-friendliness. Although …
Low-power audio keyword spotting using tsetlin machines
The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has
revolutionized human to machine interaction. Yet, the challenge of end-to-end energy …
revolutionized human to machine interaction. Yet, the challenge of end-to-end energy …
On the convergence of tsetlin machines for the XOR operator
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct
properties, including transparent inference and learning using hardware-near building …
properties, including transparent inference and learning using hardware-near building …
Building concise logical patterns by constraining Tsetlin Machine clause size
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 …
advantages of being transparent and hardware-friendly. While TMs match or surpass deep …