Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention

Research article (2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022) · cited 11× · AI/ML
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Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention

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Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention is a scholarly article[1].

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  • Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention's instance of is recorded as scholarly article[2].

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APA 4ort.xyz Knowledge Graph. (2026). Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention. Retrieved May 24, 2026, from https://4ort.xyz/entity/paramixer-parameterizing-mixing-links-in-sparse-factors-works-better-than-dot-product-self-attention
MLA “Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/paramixer-parameterizing-mixing-links-in-sparse-factors-works-better-than-dot-product-self-attention.
BibTeX @misc{4ortxyz_paramixer-parameterizing-mixing-links-in-sparse-factors-works-better-than-dot-product-self-attention_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention}}, year = {2026}, url = {https://4ort.xyz/entity/paramixer-parameterizing-mixing-links-in-sparse-factors-works-better-than-dot-product-self-attention}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention — https://4ort.xyz/entity/paramixer-parameterizing-mixing-links-in-sparse-factors-works-better-than-dot-product-self-attention (retrieved 2026-05-24)

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