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Methods/Content-based Attention

Content-based Attention

GeneralIntroduced 200033 papers
Source Paper

Description

Content-based attention is an attention mechanism based on cosine similarity:

fatt(hi,s_j)=cos⁡[h_i;s_j]f_{att}\left(\textbf{h}_{i}, \textbf{s}\_{j}\right) = \cos\left[\textbf{h}\_{i};\textbf{s}\_{j}\right] fatt​(hi​,s_j)=cos[h_i;s_j]

It was utilised in Neural Turing Machines as part of the Addressing Mechanism.

We produce a normalized attention weighting by taking a softmax over these attention alignment scores.

Papers Using This Method

Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security2025-04-10Memory-augmented conformer for improved end-to-end long-form ASR2023-09-22FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory2023-08-20TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition2023-05-09Token Turing Machines2022-11-16Similarity and Content-based Phonetic Self Attention for Speech Recognition2022-03-19HiMA: A Fast and Scalable History-based Memory Access Engine for Differentiable Neural Computer2022-02-15AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks2022-01-23Hard-Attention for Scalable Image Classification2021-02-20Robust High-dimensional Memory-augmented Neural Networks2020-10-05Explainable Inference on Sequential Data via Memory-Tracking2020-07-11Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR2020-02-14Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer2019-10-22Memory-Augmented Recurrent Networks for Dialogue Coherence2019-10-16A Neural Turing~Machine for Conditional Transition Graph Modeling2019-07-15Understanding Memory Modules on Learning Simple Algorithms2019-07-01A review on Neural Turing Machine2019-04-10Few-Shot Generalization Across Dialogue Tasks2018-11-28Context-Aware Neural Model for Temporal Information Extraction2018-07-01A Taxonomy for Neural Memory Networks2018-05-01