SeqCondenser: Inductive Representation Learning of Sequences by Sampling Characteristic Functions

In this work, we introduce SeqCondenser, a neural network layer that compresses a variable-length input sequence into a fixed-size vector representation. The SeqCondenser layer samples the empirical characteristic function and its derivatives for each input dimension, and uses an attention mechanism to determine the associated probability distribution. We argue that the features extracted through this process effectively represent the entire sequence and that the SeqCondenser layer is particularly well-suited for inductive sequence classification tasks, such as text and time series classification. Our experiments show that SCoMo, a SeqCondenser-based architecture, outperforms the state-of-the-art inductive methods on nearly all examined text classification datasets and also outperforms the current best transductive method on one dataset.