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

Yongkang Li

SIGIR 2026×3AAAI 2026NeurIPS 2026效率与系统检索与知识评测与基准代码可解释性
新星分新星
86
一作贡献30
核心/参与49
方向广度16
合作网络12
1 一作 · 5 篇 · SIGIR 2026 / AAAI 2026 / NeurIPS 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

5
作者位次 #2/5SIGIR 2026Oral· Information Retrieval (Reproducibility)

Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval

Arne Eichholtz · Yongkang Li · Jutte Vijverberg · Tobias Groot · Mohammad Aliannejadi

摘要

The Hypencoder, proposed by Killingback et al., is a retrieval framework that replaces the fixed inner-product scoring function used in standard bi-encoders with a query-specific neural network (the $q$-net), whose weights are generated by a hypernetwork from the contextualized query embeddings....

效率与系统评测与基准检索与知识OpenReview
作者位次 #2/5SIGIR 2026Oral· Information Retrieval (Reproducibility)

Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval

Kidist Amde Mekonnen · Yongkang Li · Yubao Tang · Simon Lupart · Sebastian Schelter

摘要

Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to early pruning of relevant prefixes under finite-beam decoding. Planning Ahead in Generative Retrieval (PAG) mitigates...

效率与系统检索与知识代码OpenReview
一作SIGIR 2026Poster· Information Retrieval (Short)

Spectral Tempering for Embedding Compression in Dense Passage Retrieval

Yongkang Li · Panagiotis Eustratiadis · Evangelos Kanoulas

摘要

Dimensionality reduction is critical for deploying dense retrieval systems at scale, yet mainstream post-hoc methods face a fundamental trade-off: principal component analysis (PCA) preserves dominant variance but underutilizes representational capacity, while whitening enforces isotropy at the c...

效率与系统检索与知识可解释性OpenReview
作者位次 #2/4AAAI 2026Poster· Artificial Intelligence

Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation

Yuxi Lin · Yongkang Li · Jie Xing · zipei fan

常见合作者