Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Chang, Sung-en"'
Autor:
Rupprecht, Timothy, Chang, Sung-En, Wu, Yushu, Lu, Lei, Nan, Enfu, Li, Chih-hsiang, Lai, Caiyue, Li, Zhimin, Hu, Zhijun, He, Yumei, Kaeli, David, Wang, Yanzhi
Publikováno v:
2024 Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track. Pages 8780-8783
We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we present Crowd Vote - an adaptation
Externí odkaz:
http://arxiv.org/abs/2408.04068
Autor:
Xie, Yanyue, Dong, Peiyan, Yuan, Geng, Li, Zhengang, Zabihi, Masoud, Wu, Chao, Chang, Sung-En, Zhang, Xufeng, Lin, Xue, Ding, Caiwen, Yoshikawa, Nobuyuki, Chen, Olivia, Wang, Yanzhi
Superconducting circuits, like Adiabatic Quantum-Flux-Parametron (AQFP), offer exceptional energy efficiency but face challenges in physical design due to sophisticated spacing and timing constraints. Current design tools often neglect the importance
Externí odkaz:
http://arxiv.org/abs/2407.18209
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the first effort to assign mixed quantization schemes and multiple precisions
Externí odkaz:
http://arxiv.org/abs/2111.00153
This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model compression technique. The novelty of this work is: We use a quantization
Externí odkaz:
http://arxiv.org/abs/2111.00155
Autor:
Huang, Shaoyi, Xu, Dongkuan, Yen, Ian E. H., Wang, Yijue, Chang, Sung-en, Li, Bingbing, Chen, Shiyang, Xie, Mimi, Rajasekaran, Sanguthevar, Liu, Hang, Ding, Caiwen
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However, under the trending pretrain-and-finetune paradigm, we postulate a coun
Externí odkaz:
http://arxiv.org/abs/2110.08190
Autor:
Chang, Sung-En, Li, Yanyu, Sun, Mengshu, Shi, Runbin, So, Hayden K. -H., Qian, Xuehai, Wang, Yanzhi, Lin, Xue
Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edge-computing platforms, e.g., ASICs, FPGAs, and embedded systems, are e
Externí odkaz:
http://arxiv.org/abs/2012.04240
With the tremendous success of deep learning, there exists imminent need to deploy deep learning models onto edge devices. To tackle the limited computing and storage resources in edge devices, model compression techniques have been widely used to tr
Externí odkaz:
http://arxiv.org/abs/2009.07460
Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors. The resulting optimization problem is challenging due to the non-convex obj
Externí odkaz:
http://arxiv.org/abs/1810.04754
Autor:
Yen IEH; Carnegie Mellon University, U.S.A., Lee WC; National Taiwan University, Taiwan., Chang SE; National Taiwan University, Taiwan., Suggala AS; Carnegie Mellon University, U.S.A., Lin SD; National Taiwan University, Taiwan., Ravikumar P; Carnegie Mellon University, U.S.A.
Publikováno v:
Proceedings of machine learning research [Proc Mach Learn Res] 2017 Aug; Vol. 70, pp. 3949-3957.