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Microsoft (United States)

United States

11 papers here16 citations across 11
fields
  • cs.IR2
  • quant-ph2
  • cs.AI1
  • cs.CL1
  • cs.CR1
  • cs.CV1
  • cs.LG1
  • cs.SE1
ROR 00d0nc645OpenAlex

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most citedApproximating local properties by tensor network states with constant bond dimension

11 citations

researchers with a paper here
  • Brad Lackey2 profiles2 · h 14
  • Daniel Rodríguez-Cárdenas2 profiles2 · h 6
  • David N. Palacio2 profiles2 · h 9
  • Denys Poshyvanyk2 profiles2 · h 9
  • Aarthi Sundaram1 · h 12
  • A. Buvanesh1 · h 3
  • Alejandro Velasco1 · h 4
  • Allen Kim1 · h 2
  • Amy Luers1 · h 1
  • Anna Schmedding1
  • Bhawna Paliwal1 · h 5
  • Bin Tan1 · h 2
collaborating institutions
  • University of ChicagoUS2 papers
  • William & MaryUS2 papers
  • Cisco Systems (United States)US1 paper
  • Colonial Williamsburg FoundationUS1 paper
  • Indian Institute of Technology DelhiIN1 paper
  • Microsoft Research Asia (China)CN1 paper
  • Microsoft Research (India)IN1 paper
  • National University of Defense TechnologyCN1 paper
  • National Yang Ming Chiao Tung UniversityTW1 paper
  • Peking UniversityCN1 paper
  • Princeton UniversityUS1 paper
  • Research Center for Information Technology Innovation, Academia SinicaTW1 paper
Showing cs.CLShow all

1 paper · 1 filter

cs.CL2026

Scaling Textual Gradients via Sampling-Based Momentum

Zixin Ding, Junyuan Hong, Zhan Shi +6

LLM-based prompt optimization, which uses LLM-provided ``textual gradients'' (feedback) to refine prompts, has emerged as an effective method for automatic prompt engineering. Howe…

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