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Ning Jia

4 papers hereh-index 588 citations6 works total

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

author position
  • middle author4

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

fields
  • cs.CV2
  • cs.LG1
  • eess.IV1
same name
  • Ning Jia — 4 papers, h 25
  • Ning Jia — 4 papers, h 4
  • Ning Jia — 4 papers, h 2
  • Ning Jia — 4 papers, h 7
  • Ning Jia — 2 papers, h 3
  • Ning Jia — 1 paper, h 4

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 citedTrain Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning

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

collaborators

4 papers

cs.CV2024

UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidance

Haojun Jiang, Teng Wang, Zhenguo Sun +8

Echocardiography is an essential medical technique for diagnosing cardiovascular diseases, but its high operational complexity has led to a shortage of trained professionals. To ad…

cs.CV2024

Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train

Haojun Jiang, Meng Li, Zhenguo Sun +5

The complex structure of the heart leads to significant challenges in echocardiography, especially in acquisition cardiac ultrasound images. Successful echocardiography requires a…

eess.IV2024

Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model

Haojun Jiang, Zhenguo Sun, Ning Jia +5

Echocardiography is the only technique capable of real-time imaging of the heart and is vital for diagnosing the majority of cardiac diseases. However, there is a severe shortage o…

cs.LG2023★ 1 cited

Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning

Shenzhi Wang, Qisen Yang, Jiawei Gao +6

Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-training on a pre-collected dataset with fine-tuning in an online environment. However, the i…

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