no code implementations • 4 Jan 2024 • Shangyu Wu, Ying Xiong, Yufei Cui, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue
Retrieval-based augmentations that aim to incorporate knowledge from an external database into language models have achieved great success in various knowledge-intensive (KI) tasks, such as question-answering and text generation.
Natural Language Understanding Neural Architecture Search +5
no code implementations • 22 Apr 2023 • Tsung-Han Kuo, Zhenge Jia, Tei-Wei Kuo, Jingtong Hu
With the increased accuracy of modern computer vision technology, many access control systems are equipped with face recognition functions for faster identification.
1 code implementation • 24 May 2022 • Shangyu Wu, Yufei Cui, Jinghuan Yu, Xuan Sun, Tei-Wei Kuo, Chun Jason Xue
Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI).
1 code implementation • 30 Mar 2022 • Yu Mao, Yufei Cui, Tei-Wei Kuo, Chun Jason Xue
To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors.
no code implementations • 8 Nov 2021 • Yu-Chen Lin, Cheng Yu, Yi-Te Hsu, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo
In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing.
no code implementations • 9 Jun 2021 • Yu-Chen Lin, Tsun-An Hsieh, Kuo-Hsuan Hung, Cheng Yu, Harinath Garudadri, Yu Tsao, Tei-Wei Kuo
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.
1 code implementation • CVPR 2021 • Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, Chun Jason Xue
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.
no code implementations • 17 Aug 2018 • Yi-Te Hsu, Yu-Chen Lin, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo
We evaluated the proposed EOFP quantization technique on two types of neural networks, namely, bidirectional long short-term memory (BLSTM) and fully convolutional neural network (FCN), on a speech enhancement task.