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20202026
most citedQuantitative Evaluations on Saliency Methods: An Experimental Study

14 citations · 24 across the 24 of their papers we have counts for

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6 papers · 1 filter

cs.DB2026

MERIT: Efficient In-Place Deletion for Dynamic Graph-Based Approximate Nearest Neighbor Indexes

Zekai Wu, Jiabao Jin, Peng Cheng +6

Graph-based indexes have become the dominant approach to approximate nearest neighbor search (ANNS) over high-dimensional data and play a crucial role in real-world applications su…

cs.DB2026

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization

Tianhao Tang, Haoyang Li, Lei Chen

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their immense number of parameters and complex tra…

cs.DB2026

QCFuse: Query-Centric Cache Fusion for Efficient RAG Inference

Jianxin Yan, Zeheng Qian, Wangze Ni +6

Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computational costs and improving effic…

cs.DB2025

RelServe: Fast LLM Inference Serving on Relational Data

Xin Zhang, Shihong Gao, Yanyan Shen +2

The use of Large Language Models (LLMs) for querying relational data has given rise to relQuery, a workload pattern that applies templated LLM calls to structured tables. As relQue…

cs.DB2025

LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation

Fei Teng, Haoyang Li, Lei Chen

Modern computing systems, such as HDFS and Spark, produce vast quantities of logs that developers use for tasks like anomaly detection and error analysis. To simplify log analysis,…

cs.DB2025

EnhanceGraph: A Continuously Enhanced Graph-based Index for High-dimensional Approximate Nearest Neighbor Search

Xiaoyao Zhong, Jiabao Jin, Peng Cheng +5

Recently, Approximate Nearest Neighbor Search in high-dimensional vector spaces has garnered considerable attention due to the rapid advancement of deep learning techniques. We obs…