◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Liping Jing

4 papers hereh-index 320 citations12 works total

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

author position
  • middle author2
  • last author2

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

fields
  • cs.LG3
  • cs.CL1
same name
  • Liping Jing — 13 papers
  • Liping Jing — 2 papers
  • Liping Jing — 2 papers, h 1
  • Liping Jing — 1 paper, h 2
  • Liping Jing — 1 paper, h 5
  • Liping Jing — 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

most cited1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

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

collaborators

4 papers

cs.CL2025

Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models

Yi Feng, Jiaqi Wang, Wenxuan Zhang +6

Recent progress in large language models (LLMs) has opened new possibilities for mental health support, yet current approaches lack realism in simulating specialized psychotherapy…

cs.LG2025

Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs

Chang Gao, Kang Zhao, Runqi Wang +2

Large language models (LLMs) have demonstrated impressive capabilities, but their enormous size poses significant challenges for deployment in real-world applications. To address t…

cs.LG2024

Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs

Kang Zhao, Tao Yuan, Han Bao +6

To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups…

cs.LG2024★ 1 cited

1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

Chang Gao, Jianfei Chen, Kang Zhao +2

Fully quantized training (FQT) accelerates the training of deep neural networks by quantizing the activations, weights, and gradients into lower precision. To explore the ultimate…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.