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Jiaxin Mao

12 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author6
  • last author4

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.IR7
  • cs.CL2
  • cs.CV1
  • cs.LG1
  • physics.flu-dyn1
ORCID 0000-0002-9257-5498
same name
  • Jiaxin Mao — 8 papers, h 23
  • Jiaxin Mao — 2 papers
  • Jiaxin Mao — 1 paper, h 5
  • Jiaxin Mao — 1 paper
  • Jiaxin Mao — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222024
most citedAn Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

6 citations · 17 across the 12 of their papers we have counts for

collaborators

4 papers

cs.IR2023

An Intent Taxonomy of Legal Case Retrieval

Yunqiu Shao, Haitao Li, Yueyue Wu +5

Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' inform…

cs.IR2023

Constructing Tree-based Index for Efficient and Effective Dense Retrieval

Haitao Li, Qingyao Ai, Jingtao Zhan +4

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness…

physics.flu-dyn2023★ 3 cited

RANS Simulations of Turbulent Round Jets in the Presence of Density Difference and Comparison with High-Resolution Experimental Data

Jiaxin Mao, Sunming Qin, Victor Petrov +1

In this paper, the novel experimental data reported by Qin et al. [1] are used to assess the predictive capability of the Realizable k-epsilon (RKE) model and Reynolds stress trans…

cs.IR2022★ 6 cited

Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval

Jingtao Zhan, Qingyao Ai, Yiqun Liu +4

Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can enc…

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