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researcher

Bo Liu

4 papers here

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

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL1
  • cs.CR1
  • cs.CV1
  • cs.LG1
ORCID 0000-0002-4250-6339
same name
  • Bo Liu — 12 papers
  • Bo Liu — 12 papers, h 17
  • Bo Liu — 9 papers
  • Bo Liu — 8 papers
  • Bo Liu — 8 papers
  • Bo Liu — 8 papers, h 7

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

most citedMulti-hop Commonsense Knowledge Injection Framework for Zero-Shot Commonsense Question Answering

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2024

Longhorn: State Space Models are Amortized Online Learners

Bo Liu, Rui Wang, Lemeng Wu +3

Modern large language models are built on sequence modeling via next-token prediction. While the Transformer remains the dominant architecture for sequence modeling, its quadratic…

cs.CR2024

QUEEN: Query Unlearning against Model Extraction

Huajie Chen, Tianqing Zhu, Lefeng Zhang +4

Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and usingthe query r…

cs.CV2024

Depth-aware Test-Time Training for Zero-shot Video Object Segmentation

Weihuang Liu, Xi Shen, Haolun Li +4

Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model…

cs.CL2023★ 1 cited

Multi-hop Commonsense Knowledge Injection Framework for Zero-Shot Commonsense Question Answering

Xin Guan, Biwei Cao, Qingqing Gao +3

Commonsense question answering (QA) research requires machines to answer questions based on commonsense knowledge. However, this research requires expensive labor costs to annotate…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.