Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Yang, Dingcheng"'
More accurate capacitance extraction is demanded for designing integrated circuits under advanced process technology. The pattern matching approach and the field solver for capacitance extraction have the drawbacks of inaccuracy and large computation
Externí odkaz:
http://arxiv.org/abs/2408.13195
Diffusion models have been widely deployed in various image generation tasks, demonstrating an extraordinary connection between image and text modalities. However, they face challenges of being maliciously exploited to generate harmful or sensitive i
Externí odkaz:
http://arxiv.org/abs/2402.01369
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Moreover, the transferability of the adversarial examples has received broad attention in recent years, which means that adversarial examples crafted by a surrogate
Externí odkaz:
http://arxiv.org/abs/2304.06908
Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to
Externí odkaz:
http://arxiv.org/abs/2206.08316
Autor:
Jeon, Injun, Yin, Linghong, Yang, Dingcheng, Chen, Hong, Go, Seong Won, Kang, Min Seung, Ahn, Hyung Soo, Cho, Chae-Ryong
Publikováno v:
In Journal of Energy Chemistry October 2024 97:478-485
Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology. The pattern matching based full-chip extraction methodology delivers fast computational speed, but suffers from large erro
Externí odkaz:
http://arxiv.org/abs/2107.06511
Autor:
Jeon, Injun, Kim, Taegyun, Seo, Jangwon, Jeong, Il-Kyoung, Lee, Jin Hong, Park, Minjoon, Park, Yiseul, Yang, Dingcheng, Cho, Chae Ryong
Publikováno v:
In Applied Surface Science 1 March 2024 648
Publikováno v:
In Applied Surface Science 1 February 2024 645
Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks. However, it has raised security concerns in enormous FR applications because deep CNNs are unusually vulnerable to adversarial examples, a
Externí odkaz:
http://arxiv.org/abs/2007.04118
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain the optimal solution of weight quantization and an optimization proce
Externí odkaz:
http://arxiv.org/abs/2003.09615