An n-dimensional numerical Transformer with linear embedding, bin-based discretization, and parallel output heads improves human activity recognition accuracy by 10-15% over a tokenized vanilla Transformer.
Chan Chang, R
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Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing
An n-dimensional numerical Transformer with linear embedding, bin-based discretization, and parallel output heads improves human activity recognition accuracy by 10-15% over a tokenized vanilla Transformer.