Continual Low-Rank Scaled Dot-product Attention | Paper Citation Record

Observation 83a7882d-d623-4b10-a68b-fc7d063bb216 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Continual Low-Rank Scaled Dot-product Attention Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 1

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Observation d9e8ee95-a755-45e8-b082-363e52dccca1 · outbound

This paper cites Trans- formers in the real world: A survey on NLP applications.

Continual Low-Rank Scaled Dot-product Attention Trans- formers in the real world: A survey on NLP applications

Reference 2

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Observation 309c28f1-4373-43ca-88e0-f0413ab061a8 · outbound

This paper cites Transformer architecture and attention mechanisms in genome data analysis: A comprehensive review.

Continual Low-Rank Scaled Dot-product Attention Transformer architecture and attention mechanisms in genome data analysis: A comprehensive review

Reference 3

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Observation cdbf2adc-389f-42ae-ae75-7c64cbf05300 · outbound

This paper cites Vision Transformers for Action Recognition: A Survey.

Continual Low-Rank Scaled Dot-product Attention Vision Transformers for Action Recognition: A Survey

Reference 4

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Observation 7c031925-eda3-4cab-a169-f101a01fca84 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Continual Low-Rank Scaled Dot-product Attention An image is worth 16x16 words: Transformers for image recognition at scale

Reference 5

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Observation 08f7b4d3-2a99-4f6c-8ad6-3359775363da · outbound

This paper cites Singh, Muskaan Chopra, Sudhakar Kumar, and Francesco Colace.

Continual Low-Rank Scaled Dot-product Attention Singh, Muskaan Chopra, Sudhakar Kumar, and Francesco Colace

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:42.064666Z

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Observation 8dcf52df-da94-4a1a-bbe6-8c7341c60f5e · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 7

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Observation c633bb72-ae33-4ffb-acac-0e1585a0e699 · outbound

This paper cites A Survey of Deep Learning and Foundation Models for Time Series Forecasting.

Continual Low-Rank Scaled Dot-product Attention A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 8

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Observation 1320ca10-055f-4a0d-93b5-a468a428c8e5 · outbound

This paper cites Single-layer Vision Transformers for more accurate early exits with less overhead.

Continual Low-Rank Scaled Dot-product Attention Single-layer Vision Transformers for more accurate early exits with less overhead

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.957365Z

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Observation 21a2c198-fe2b-4800-a2fa-e56f3f188726 · outbound

This paper cites Efficient High-Resolution Deep Learning: A Survey.

Continual Low-Rank Scaled Dot-product Attention Efficient High-Resolution Deep Learning: A Survey

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.914753Z

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Observation 2a48665e-950a-4eaa-b1ee-1195e1c47984 · outbound

This paper cites Reducing transformer depth on demand with structured dropout.

Continual Low-Rank Scaled Dot-product Attention Reducing transformer depth on demand with structured dropout

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.798661Z

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Observation 47b7b396-2cc8-4173-9b52-834147af03f7 · outbound

This paper cites Transformer Multivariate Forecasting: Less is More?.

Continual Low-Rank Scaled Dot-product Attention Transformer Multivariate Forecasting: Less is More?

Reference 12

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Observation 1381d7d6-c38f-4296-9cbf-46b3f1961653 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

Continual Low-Rank Scaled Dot-product Attention Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.757602Z

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Observation 30e07cca-337d-4c57-95b1-2a9e25c3d1d0 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Continual Low-Rank Scaled Dot-product Attention Generating Long Sequences with Sparse Transformers

Reference 14

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Observation 657ca656-b3a5-445c-b4b0-a702e519e3b8 · outbound

This paper cites Longformer: The Long-Document Transformer.

Continual Low-Rank Scaled Dot-product Attention Longformer: The Long-Document Transformer

Reference 15

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Observation 18ed6feb-b2d0-4764-a462-022723270451 · outbound

This paper cites Colwell, and Adrian Weller.

Continual Low-Rank Scaled Dot-product Attention Colwell, and Adrian Weller

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.722643Z

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Observation 33154db5-73dc-4163-be7d-057b180bb6fc · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:44:41.691539Z

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Observation 0c206191-9b08-4ce4-9273-d89ee9b659bd · outbound

This paper cites On compressing deep models by low rank and sparse decomposition.

Continual Low-Rank Scaled Dot-product Attention On compressing deep models by low rank and sparse decomposition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.619889Z

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Observation 482af051-da69-4fec-8bb6-bcb2d36bc70f · outbound

This paper cites Nystr ¨omformer: A nystr ¨om- based algorithm for approximating self-attention.

Continual Low-Rank Scaled Dot-product Attention Nystr ¨omformer: A nystr ¨om- based algorithm for approximating self-attention

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.518206Z

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Observation eb233905-3fc3-4df1-8bd8-3d133d3b2ae4 · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 20

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Observation 7fec859d-ce3d-4cd7-a76e-129963bb2511 · outbound

This paper cites Cvt: Introducing convolutions to vision transformers.

Continual Low-Rank Scaled Dot-product Attention Cvt: Introducing convolutions to vision transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.438242Z

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Observation 8f04936d-ae7b-4c44-ab1c-581c45bfba35 · outbound

This paper cites Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah.

Continual Low-Rank Scaled Dot-product Attention Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.400138Z

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Observation 9325c7c2-9068-4b8a-8a04-e45a2970a68a · outbound

This paper cites Is space-time attention all you need for video understanding? In International Conference on Machine Learning , pages 813–824, 2021.

Continual Low-Rank Scaled Dot-product Attention Is space-time attention all you need for video understanding? In International Conference on Machine Learning , pages 813–824, 2021

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.229959Z

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Observation 244c4f92-1030-4139-a755-de2cbb375f73 · outbound

This paper cites Video swin transformer.

Continual Low-Rank Scaled Dot-product Attention Video swin transformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.159513Z

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Observation cf1ba3af-7d2f-4c83-aacd-caffe87114eb · outbound

This paper cites Vivit: A video vision transformer.

Continual Low-Rank Scaled Dot-product Attention Vivit: A video vision transformer

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.073936Z

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Observation 9dc1b33e-e954-4c81-9d43-c3e96c4862ba · outbound

This paper cites Multiscale vision transformers.

Continual Low-Rank Scaled Dot-product Attention Multiscale vision transformers

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:41.039577Z

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Observation e85fd8f0-7c47-4960-875d-f2e66f881132 · outbound

This paper cites Continual inference: A library for efficient online inference with deep neural networks in pytorch.

Continual Low-Rank Scaled Dot-product Attention Continual inference: A library for efficient online inference with deep neural networks in pytorch

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.874754Z

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Observation 441c2d58-0a39-4922-b48e-94bf01d15d6b · outbound

This paper cites Recurrent Neural Networks (RNNs): A gentle Introduction and Overview.

Continual Low-Rank Scaled Dot-product Attention Recurrent Neural Networks (RNNs): A gentle Introduction and Overview

Reference 28

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Observation b0d4dba8-0bcf-44ad-a636-4c1860f81a45 · outbound

This paper cites Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network.

Continual Low-Rank Scaled Dot-product Attention Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Reference 29

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Observation 2044053f-ad31-4bb8-8800-96f6f7ac2bfb · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Continual Low-Rank Scaled Dot-product Attention Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 30

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Observation d264e9e3-0d8e-4735-8b9a-3f2a6a9d6d70 · outbound

This paper cites Con- tinual transformers: Redundancy-free attention for online inference.

Continual Low-Rank Scaled Dot-product Attention Con- tinual transformers: Redundancy-free attention for online inference

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.788814Z

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Observation b0f1d5b5-ebc6-414a-b48f-f7ce3bf69179 · outbound

This paper cites Continual spatio-temporal graph convolutional networks.

Continual Low-Rank Scaled Dot-product Attention Continual spatio-temporal graph convolutional networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.757538Z

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Observation 8d2f63dd-9cd7-4759-a86b-49399ca1f81d · outbound

This paper cites Continual 3d convolutional neural networks for real-time processing of videos.

Continual Low-Rank Scaled Dot-product Attention Continual 3d convolutional neural networks for real-time processing of videos

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.728866Z

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Observation 8a4844b2-ca53-42ac-b7e9-81674a1eab92 · outbound

This paper cites Vision Xformers: Efficient Attention for Image Classification.

Continual Low-Rank Scaled Dot-product Attention Vision Xformers: Efficient Attention for Image Classification

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:44:39.810327Z

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Observation df2aa885-c7d9-4d02-b71f-dd91d41e02d2 · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:44:40.704544Z

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Observation bd29012b-19ed-4aa8-9190-82c481547247 · outbound

This paper cites Improving CUR matrix decomposition and the nystr ¨om approximation via adaptive sampling.

Continual Low-Rank Scaled Dot-product Attention Improving CUR matrix decomposition and the nystr ¨om approximation via adaptive sampling

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.664219Z

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Observation 45102145-f9a7-42e6-9079-5fad17f40a35 · outbound

This paper cites Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and Jo˜ao F.

Continual Low-Rank Scaled Dot-product Attention Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and Jo˜ao F

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.634763Z

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Observation 620357d3-163d-4eb3-a11f-f13997d261b3 · outbound

This paper cites Eventful transformers: Leveraging temporal redundancy in vision transformers.

Continual Low-Rank Scaled Dot-product Attention Eventful transformers: Leveraging temporal redundancy in vision transformers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.573467Z

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Observation 480e11bc-f926-4ba9-a14d-ba55add0535f · outbound

This paper cites SOFT: softmax-free transformer with linear complexity.

Continual Low-Rank Scaled Dot-product Attention SOFT: softmax-free transformer with linear complexity

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.549808Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9bde083f-78fd-4cf1-b7fd-a96e666e84c3 · outbound

This paper cites Adaptive multi-resolution attention with linear complexity.

Continual Low-Rank Scaled Dot-product Attention Adaptive multi-resolution attention with linear complexity

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.526966Z

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Observation 14bcae77-5745-44ec-aa1a-0621c15731f8 · outbound

This paper cites Kwok, Slobodan Vucetic, and Bahram Parvin.

Continual Low-Rank Scaled Dot-product Attention Kwok, Slobodan Vucetic, and Bahram Parvin

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.504483Z

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Observation 2187b7f2-73f8-41c2-92d7-025cd52f77b3 · outbound

This paper cites Scaling Up Class-Specific Kernel Discriminant Analysis for Large-Scale Face Verification.

Continual Low-Rank Scaled Dot-product Attention Scaling Up Class-Specific Kernel Discriminant Analysis for Large-Scale Face Verification

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.490992Z

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Observation 713ab673-31da-436a-9487-5088d642b9c8 · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:44:40.468909Z

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Observation 966c9101-4ecb-47bf-9b51-f5114dfed757 · outbound

This paper cites Razavi, A.

Continual Low-Rank Scaled Dot-product Attention Razavi, A

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.444590Z

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Observation 1ce32579-6c34-40c5-853d-c2b6589cac75 · outbound

This paper cites Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala.

Continual Low-Rank Scaled Dot-product Attention Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.411820Z

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Observation 3f276387-f333-4237-8a4f-81244191b6fd · outbound

This paper cites Deep Learning.

Continual Low-Rank Scaled Dot-product Attention Deep Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T22:44:39.077024Z

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Observation 89eb0dd3-f32d-46b6-bfba-9a2b66c8a1fb · outbound

This paper cites A scale for the measurement of the psychological magnitude pitch.

Continual Low-Rank Scaled Dot-product Attention A scale for the measurement of the psychological magnitude pitch

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.373758Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b540d7a-cd01-4458-bbf1-a159ad915c63 · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:44:40.357671Z

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Observation 246b5cb2-cd41-4610-80b0-526f39841219 · outbound

This paper cites Rethinking CNN Models for Audio Classification.

Continual Low-Rank Scaled Dot-product Attention Rethinking CNN Models for Audio Classification

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T22:44:39.145701Z

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Observation b45f7573-7419-4ec0-b994-4f31ada548cd · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:44:40.338794Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce5d047b-73b8-477f-8287-a4cd9120388c · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Continual Low-Rank Scaled Dot-product Attention Very deep convolutional networks for large-scale image recognition

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T22:44:39.194753Z

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Observation 8ea7da11-24b8-41ff-8603-654785156cf4 · outbound

This paper cites an unresolved cited work.

Continual Low-Rank Scaled Dot-product Attention Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:44:40.298730Z

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Observation aa2b296a-426a-4a16-b7ac-524fce1674db · outbound

This paper cites Online action detection.

Continual Low-Rank Scaled Dot-product Attention Online action detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.281898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3f8c1463-21a2-4cb9-a3c2-33e8f22e3d3d · outbound

This paper cites Zamir, Yu-Gang Jiang, Alex Gorban, Ivan Laptev, Rahul Sukthankar, and Mubarak Shah.

Continual Low-Rank Scaled Dot-product Attention Zamir, Yu-Gang Jiang, Alex Gorban, Ivan Laptev, Rahul Sukthankar, and Mubarak Shah

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.261641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d644c828-e7e8-4883-a254-386ce7b32b06 · outbound

This paper cites Learning to discriminate information for online action detection: Analysis and application.

Continual Low-Rank Scaled Dot-product Attention Learning to discriminate information for online action detection: Analysis and application

Reference 55

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d484079c-0c8a-4fa6-9de2-1ecc11c7a361 · outbound

This paper cites Temporal segment networks for action recognition in videos.

Continual Low-Rank Scaled Dot-product Attention Temporal segment networks for action recognition in videos

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.206663Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c2dd5313-df5e-4812-a86a-4496c3463baa · outbound

This paper cites Activitynet: A large-scale video benchmark for human activity understanding.

Continual Low-Rank Scaled Dot-product Attention Activitynet: A large-scale video benchmark for human activity understanding

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.178237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ddec96f0-c937-4bec-afda-fb7fde1d1809 · outbound

This paper cites Quo vadis, action recognition? A new model and the kinetics dataset.

Continual Low-Rank Scaled Dot-product Attention Quo vadis, action recognition? A new model and the kinetics dataset

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.156896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1c5ca38-4526-459f-ad8e-fb9378444d07 · outbound

This paper cites ElectricityLoadDiagrams20112014.

Continual Low-Rank Scaled Dot-product Attention ElectricityLoadDiagrams20112014

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T22:44:39.594853Z

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This paper cites Decoupled weight decay regular- ization.

Continual Low-Rank Scaled Dot-product Attention Decoupled weight decay regular- ization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:44:40.124750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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