In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing
Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both
an absorbing Exclude- and an absorbing Include state, making the learning
scheme absorbing instead of ergodic. When a TA reaches an absorbing state, it
will never leave that state again. If the absorbing state is an Exclude state,
both the automaton and the literal can be removed from further consideration.
The literal will as a result never participates in that clause. If the
absorbing state is an Include state, on the other hand, the literal is stored
as a permanent part of the clause while the TA is discarded. A novel sparse
data structure supports these updates by means of three action lists: Absorbed
Include, Include, and Exclude. By updating these lists, the TM gets smaller and
smaller as the literals and their TA withdraw. In this manner, the computation
accelerates during learning, leading to faster learning and less energy
consumption.

通过引入具有吸收状态的稀疏 Tsetlin Machine (TM) 以及吸收态的 Tsetlin Automata (TA)，本文提出了一种新的学习方案，通过稀疏数据结构的更新，在学习过程中加速计算、提高学习速度并减少能源消耗。

具吸收状态的 Contracting Tsetlin Machine 与自动机

Contracting Tsetlin Machine with Absorbing Automata

State-of-the-art neural network models for optical flow estimation require a
dense correlation volume at high resolutions for representing per-pixel
displacement. Although the dense correlation volume is informative for accurate
estimation, its heavy computation and memory usage hinders the efficient
training and deployment of the models. In this paper, we show that the dense
correlation volume representation is redundant and accurate flow estimation can
be achieved with only a fraction of elements in it. Based on this observation,
we propose an alternative displacement representation, named Sparse Correlation
Volume, which is constructed directly by computing the k closest matches in one
feature map for each feature vector in the other feature map and stored in a
sparse data structure. Experiments show that our method can reduce
computational cost and memory use significantly, while maintaining high
accuracy compared to previous approaches with dense correlation volumes. Code
is available at this https URL .

本文研究了一种神经网络模型的新方法，使用稀疏相关物体代替密集相关物体，以更高效且准确地估算像素位移并降低计算成本和内存使用。