paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
CR

Cosimo Rulli

SIGIR 2026×2效率与系统检索与知识推理可解释性
新星分贡献者
43
一作贡献0
核心/参与17
方向广度8
合作网络6
0 一作 · 2 篇 · SIGIR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
作者位次 #2/5SIGIR 2026Poster· Information Retrieval (Short)

Sparton: Fast and Memory-Efficient Triton Kernel for Learned Sparse Retrieval

Thong Nguyen · Cosimo Rulli · Franco Maria Nardini · Rossano Venturini · Andrew Yates

摘要

State-of-the-art Learned Sparse Retrieval (LSR) models, such as \splade, typically employ a Language Modeling (LM) head to project latent hidden states into a lexically-anchored logit matrix. This intermediate matrix is subsequently transformed into a sparse lexical representation through element...

效率与系统检索与知识推理OpenReview
作者位次 #3/4SIGIR 2026Poster· Information Retrieval (Short)

Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing

Silvio Martinico · Franco Maria Nardini · Cosimo Rulli · Rossano Venturini

摘要

Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs substantial computational and memory costs. Current solutions---based on the well-known κ-means clustering algorithm---group similar vectors together t...

效率与系统检索与知识可解释性OpenReview

常见合作者