1 code implementation • 25 Mar 2024 • Jiasheng Ye, Peiju Liu, Tianxiang Sun, Yunhua Zhou, Jun Zhan, Xipeng Qiu
Pretraining data of large language models composes multiple domains (e. g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models.
1 code implementation • 19 Feb 2024 • Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yugang Jiang, Xipeng Qiu
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music.
1 code implementation • 24 Jan 2024 • Dong Zhang, Xin Zhang, Jun Zhan, ShiMin Li, Yaqian Zhou, Xipeng Qiu
It comprises an autoregressive model based on LLM for semantic information modeling and a non-autoregressive model employing flow matching for perceptual information modeling.
1 code implementation • 18 May 2023 • Dong Zhang, ShiMin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, Xipeng Qiu
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.
no code implementations • 1 Nov 2022 • Jun Zhan, Chengkun Wu, Canqun Yang, Qiucheng Miao, Xiandong Ma
In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS, learning heterogeneous structure information from a mass of unlabeled time-series data to improve the accuracy of anomaly detection, and using attention coefficient to provide an explanation for the detected anomalies.
1 code implementation • 19 Aug 2022 • Qiucheng Miao, Chuanfu Xu, Jun Zhan, Dong zhu, Chengkun Wu
Anomaly detection of multivariate time series is meaningful for system behavior monitoring.
no code implementations • 15 Oct 2020 • Liwei Hu, Yu Xiang, Jun Zhan, Zifang Shi, Wenzheng Wang
Predicting high-speed data is more difficult than predicting low-speed data, owing to that the number of high-speed data is limited, i. e. the quality of the Burgers' dataset is not satisfactory.
36 code implementations • 8 Dec 2015 • Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, Zhenyao Zhu
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.