Tsetlin Machines (TMs) provide a fundamental shift from arithmetic-based to logic-based machine learning. Supporting convolution, they deal successfully with image classification datasets like MNIST, Fashion-MNIST, and CIFAR-2. However, the TM struggles with getting state-of-the-art performance on CIFAR-10 and CIFAR-100, representing more complex tasks. This paper introduces plug-and-play collaboration between specialized TMs, referred to as TM Composites. The collaboration relies on a TM's ability to specialize during learning and to assess its competence during inference. When teaming up, the most confident TMs make the decisions, relieving the uncertain ones. In this manner, a TM Composite becomes more competent than its members, benefiting from their specializations. The collaboration is plug-and-play in that members can be combined in any way, at any time, without fine-tuning. We implement three TM specializations in our empirical evaluation: Histogram of Gradients, Adaptive Gaussian Thresholding, and Color Thermometers. The resulting TM Composite increases accuracy on Fashion-MNIST by two percentage points, CIFAR-10 by twelve points, and CIFAR-100 by nine points, yielding new state-of-the-art results for TMs. Overall, we envision that TM Composites will enable an ultra-low energy and transparent alternative to state-of-the-art deep learning on more tasks and datasets.

翻译：Tsetlin机（TM）实现了从基于算术的机器学习到基于逻辑的机器学习的根本性转变。通过支持卷积操作，TM成功处理了MNIST、Fashion-MNIST和CIFAR-2等图像分类数据集。然而，在面对更复杂任务（如CIFAR-10和CIFAR-100）时，TM难以达到最先进的性能。本文提出了一种专用TM之间的即插即用协作方法，称为TM组合体（TM Composites）。该协作机制依赖于TM在学习过程中实现专业化，以及在推理过程中评估自身能力的能力。当组成团队时，最自信的TM负责决策，从而减轻不确定TM的负担。通过这种方式，TM组合体能够利用成员的专业化优势，比单一成员更具胜任力。这种协作是即插即用的，成员可以在任何时间以任意方式组合，无需微调。我们在实证评估中实现了三种TM专业化方法：梯度直方图、自适应高斯阈值和彩色温度计。由此产生的TM组合体在Fashion-MNIST上准确率提升两个百分点，在CIFAR-10上提升十二个百分点，在CIFAR-100上提升九个百分点，为TM创造了新的最先进结果。总体而言，我们预计TM组合体将在更多任务和数据集上，为最先进的深度学习提供一种超低能耗且透明的替代方案。