no code implementations • 4 Jan 2024 • JunFeng Hou, Peiyao Wang, Jincheng Zhang, Meng Yang, Minwei Feng, Jingcheng Yin
Deploying end-to-end speech recognition models with limited computing resources remains challenging, despite their impressive performance.
1 code implementation • 24 May 2023 • Ziwei He, Meng Yang, Minwei Feng, Jingcheng Yin, Xinbing Wang, Jingwen Leng, Zhouhan Lin
Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models.
Ranked #1 on Open-Domain Question Answering on ELI5
52 code implementations • 9 Mar 2017 • Zhouhan Lin, Minwei Feng, Cicero Nogueira dos santos, Mo Yu, Bing Xiang, Bo-Wen Zhou, Yoshua Bengio
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
no code implementations • WS 2015 • Markus Freitag, Jan-Thorsten Peter, Stephan Peitz, Minwei Feng, Hermann Ney
In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network.
no code implementations • 18 Nov 2016 • Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bo-Wen Zhou, Fei Wang
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
no code implementations • 3 Nov 2015 • Minwei Feng, Bing Xiang, Bo-Wen Zhou
This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification.
2 code implementations • 7 Aug 2015 • Minwei Feng, Bing Xiang, Michael R. Glass, Lidan Wang, Bo-Wen Zhou
We apply a general deep learning framework to address the non-factoid question answering task.