1 code implementation • 27 May 2022 • Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang
We propose a general and efficient framework to control auto-regressive generation models with NeurAlly-Decomposed Oracle (NADO).
no code implementations • 18 May 2021 • Sidi Lu, Xin Yuan, Aggelos K Katsaggelos, Weisong Shi
We apply reinforcement learning to video compressive sensing to adapt the compression ratio.
no code implementations • 12 Feb 2021 • Sidi Lu, Tao Meng, Nanyun Peng
We propose InsNet, an expressive insertion-based text generator with efficient training and flexible decoding (parallel or sequential).
no code implementations • 30 Sep 2020 • Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, Weisong Shi
The recent proliferation of computing technologies, e. g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e. g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Distributed, Parallel, and Cluster Computing Robotics
1 code implementation • 17 Nov 2019 • Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis, Tong Shen, Pei Yu, Dimitrios Lymberopoulos, Sidi Lu, Weisong Shi, Xiang Chen
In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.
no code implementations • 17 Jun 2019 • Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu
In this paper, we propose a hybrid inference algorithm, the Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both search-based and data-driven methods.
no code implementations • 5 Jun 2019 • Xingzhou Zhang, Yifan Wang, Sidi Lu, Liangkai Liu, Lanyu Xu, Weisong Shi
At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services.
2 code implementations • ICLR 2019 • Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Wei-Nan Zhang, Yong Yu
In this paper, we study the generative models of sequential discrete data.
no code implementations • 15 Mar 2018 • Sidi Lu, Yaoming Zhu, Wei-Nan Zhang, Jun Wang, Yong Yu
This paper presents a systematic survey on recent development of neural text generation models.
1 code implementation • 6 Feb 2018 • Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Wei-Nan Zhang, Jun Wang, Yong Yu
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.
6 code implementations • 24 Sep 2017 • Jiaxian Guo, Sidi Lu, Han Cai, Wei-Nan Zhang, Yong Yu, Jun Wang
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
Ranked #1 on Text Generation on COCO Captions