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Found 8 papers, 3 papers with code
Trainable Fractional Fourier Transform
Recently, the fractional Fourier transform (FrFT) has been integrated into distinct deep neural network (DNN) models such as transformers, sequence models, and convolutional neural networks (CNNs).
Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers
FNet achieves competitive performances concerning the original transformer encoder model while accelerating training process by removing the computational burden of the attention mechanism.
Unsupervised Simplification of Legal Texts
While a recent study proposed a rule-based TS method for legal text, learning-based TS in the legal domain has not been considered previously.
Joint Time-Vertex Fractional Fourier Transform
The JFRT enables fractional analysis for joint time-vertex processing by extending Fourier analysis to fractional orders in both temporal and vertex domains.
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance
We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences.
Graph Signal Processing: Vertex Multiplication
Vertex multiplication, which generalizes the coordinate multiplication operation in time series signals, can be interpreted as an operator which assigns a coordinate structure to a graph.
Deep Iterative Reconstruction for Phase Retrieval
First, a DNN is trained to remove the HIO artifacts and is used iteratively with the HIO method to improve the reconstructions.
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
In other words, we align words that are already determined to be related, along predefined concepts.
