no code implementations • 1 Jan 2021 • Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Da-Cheng Juan, Jia-Yu Pan, Wei Wei
Current practices to apply temperature scaling assume either a fixed, or a manually-crafted dynamically changing schedule.
no code implementations • 25 Dec 2020 • Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Chang Juan
Temperature scaling has been widely used as an effective approach to control the smoothness of a distribution, which helps the model performance in various tasks.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, Jia-Yu Pan
Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms.
no code implementations • 8 Jul 2020 • Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, Da-Cheng Juan
In this work, we propose a new regularization technique, Remix, that relaxes Mixup's formulation and enables the mixing factors of features and labels to be disentangled.
no code implementations • 7 Jul 2020 • Li-Huang Tsai, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Cheng Juan
In this paper, we propose a noise-agnostic method to achieve robust neural network performance against any noise setting.
no code implementations • 17 Nov 2019 • Hao-Yun Chen, Li-Huang Tsai, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in semantic segmentation.
no code implementations • ICLR 2019 • Yu-Yi Su, Yung-Chih Chen, Xiang-Xiu Wu, Shih-Chieh Chang
We propose to set a checkpoint in the MAC process to determine whether a filter could terminate early based on the intermediate result.
2 code implementations • ICCV 2019 • Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model.
1 code implementation • ICLR 2019 • Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes.
no code implementations • 29 Aug 2018 • An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.
no code implementations • 27 Jun 2018 • Chi-Hung Hsu, Shu-Huan Chang, Jhao-Hong Liang, Hsin-Ping Chou, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures.
no code implementations • 14 Aug 2017 • You-Luen Lee, Da-Cheng Juan, Xuan-An Tseng, Yu-Ting Chen, Shih-Chieh Chang
When will a server fail catastrophically in an industrial datacenter?