3 citations · 10 across the 7 of their papers we have counts for
7 papers
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang +7
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution…
Robust Neural Architecture Search
Xunyu Zhu, Jian Li, Yong Liu +1
Neural Architectures Search (NAS) becomes more and more popular over these years. However, NAS-generated models tends to suffer greater vulnerability to various malicious attacks.…
Quantum adversarial metric learning model based on triplet loss function
Yan-Yan Hou, Jian Li, Xiu-Bo Chen +1
Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric l…
Learning from Noisy Labels with Decoupled Meta Label Purifier
Yuanpeng Tu, Boshen Zhang, Yuxi Li +5
Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-lear…
Information extraction and artwork pricing
Jaehyuk Choi, Lan Ju, Jian Li +1
Traditional art pricing models often lack fine measurements of painting content. This paper proposes a new content measurement: the Shannon information quantity measured by the sin…
Operation-level Progressive Differentiable Architecture Search
Xunyu Zhu, Jian Li, Yong Liu +1
Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low com…