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Zhengya Zhang

4 papers hereh-index 6133 citations22 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.AR1
  • cs.CV1
  • cs.LG1
  • eess.SP1
same name
  • Zhengya Zhang — 5 papers, h 33
  • Zhengya Zhang — 1 paper, h 2

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 citedTurbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

eess.SP2025

Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization

Ilayda Yaman, Guoda Tian, Dino Pjanic +4

Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that efficiently adapt to varying signal conditions and environmental chang…

cs.CV2025

SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest

Jack Erhardt, Ziang Li, Reid Pinkham +2

Machine learning algorithms have enabled high quality stereo depth estimation to run on Augmented and Virtual Reality (AR/VR) devices. However, high energy consumption across the f…

cs.AR2024

Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity

Dongyun Kam, Myeongji Yun, Sunwoo Yoo +3

Low bit-precisions and their bit-slice sparsity have recently been studied to accelerate general matrix-multiplications (GEMM) during large-scale deep neural network (DNN) inferenc…

cs.LG2024★ 1 cited

Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters

Yixin Song, Haotong Xie, Zhengyan Zhang +4

Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However…

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