GraphTM uses message passing on graphs to build nested deep clauses, achieving 3.86% higher accuracy than convolutional TM on CIFAR-10 and competitive results on action tracking, recommendations, and genome sequences.
Character-level convolutional networks for text classification
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
Sinkhorn-normalized doubly stochastic attention preserves rank more effectively than Softmax row-stochastic attention, with both showing doubly exponential rank decay to one with network depth.
citing papers explorer
-
The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs
GraphTM uses message passing on graphs to build nested deep clauses, achieving 3.86% higher accuracy than convolutional TM on CIFAR-10 and competitive results on action tracking, recommendations, and genome sequences.
-
Sinkhorn doubly stochastic attention rank decay analysis
Sinkhorn-normalized doubly stochastic attention preserves rank more effectively than Softmax row-stochastic attention, with both showing doubly exponential rank decay to one with network depth.