How Structure-Aware Sparse Attention Boosts Long-Code Transformers

The SASA model, a structure‑aware sparse‑attention Transformer developed by Alibaba Cloud PAI and Prof. Gao Ming’s team, improves long‑code sequence processing by sparsifying self‑attention using top‑k frequency and AST pattern matrices, achieving higher performance and lower memory/computation costs on CodeXGLUE benchmarks.

ASTCode UnderstandingLong Sequences

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How Structure-Aware Sparse Attention Boosts Long-Code Transformers

Kuaishou Tech

Jul 7, 2021 · Artificial Intelligence

SURGE: A Graph Neural Network Based Sequential Recommendation Framework

The SURGE framework leverages graph neural networks to construct and pool interest graphs from user interaction sequences, achieving stable and fast convergence, robust long‑sequence modeling, and significant performance gains over existing sequential recommendation methods on e‑commerce and short‑video datasets.

Long SequencesSURGEgraph neural networks

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SURGE: A Graph Neural Network Based Sequential Recommendation Framework