FRACTAL integrates fractional recurrent architecture into SSMs using a tunable singularity index to capture multi-scale temporal features, reporting 87.11% average on Long Range Arena and outperforming S5.
citation dossier
A structured self-attentive sentence embedding.arXiv preprint arXiv:1703.03130
5Pith papers citing it
5reference links
cs.CLtop field · 2 papers
UNVERDICTEDtop verdict bucket · 4 papers
why this work matters in Pith
Pith has found this work in 5 reviewed papers. Its strongest current cluster is cs.CL (2 papers). The largest review-status bucket among citing papers is UNVERDICTED (4 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
Graph Attention Networks compute learnable attention coefficients over node neighborhoods to produce weighted feature aggregations, achieving state-of-the-art results on citation networks and inductive protein-protein interaction graphs.
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
Pith review generated a malformed one-line summary.
citing papers explorer
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FRACTAL: SSM with Fractional Recurrent Architecture for Computational Temporal Analysis of Long Sequences
FRACTAL integrates fractional recurrent architecture into SSMs using a tunable singularity index to capture multi-scale temporal features, reporting 87.11% average on Long Range Arena and outperforming S5.
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Graph Attention Networks
Graph Attention Networks compute learnable attention coefficients over node neighborhoods to produce weighted feature aggregations, achieving state-of-the-art results on citation networks and inductive protein-protein interaction graphs.
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Learning Shared Sentiment Prototypes for Adaptive Multimodal Sentiment Analysis
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
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Universal Transformers
Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
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Attention Is All You Need
Pith review generated a malformed one-line summary.