162 citations · 471 across the 8 of their papers we have counts for
9 papers
Reinforcement Learning for Adaptive Video Compressive Sensing
Sidi Lu, Xin Yuan, Aggelos K Katsaggelos +1
We apply reinforcement learning to video compressive sensing to adapt the compression ratio. Specifically, video snapshot compressive imaging (SCI), which captures high-speed video…
Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
Liangkai Liu, Sidi Lu, Ren Zhong +4
The recent proliferation of computing technologies (e.g., sensors, computer vision, machine learning, and hardware acceleration), and the broad deployment of communication mechanis…
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
Fuxun Yu, Di Wang, Yinpeng Chen +7
Current state-of-the-art object detectors can have significant performance drop when deployed in the wild due to domain gaps with training data. Unsupervised Domain Adaptation (UDA…
OpenEI: An Open Framework for Edge Intelligence
Xingzhou Zhang, Yifan Wang, Sidi Lu +3
In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and…
Neurally-Guided Structure Inference
Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum +1
Most structure inference methods either rely on exhaustive search or are purely data-driven. Exhaustive search robustly infers the structure of arbitrarily complex data, but it is…
CoT: Cooperative Training for Generative Modeling of Discrete Data
Sidi Lu, Lantao Yu, Siyuan Feng +3
In this paper, we study the generative models of sequential discrete data. To tackle the exposure bias problem inherent in maximum likelihood estimation (MLE), generative adversari…
Neural Text Generation: Past, Present and Beyond
Sidi Lu, Yaoming Zhu, Weinan Zhang +2
This paper presents a systematic survey on recent development of neural text generation models. Specifically, we start from recurrent neural network language models with the tradit…
Texygen: A Benchmarking Platform for Text Generation Models
Yaoming Zhu, Sidi Lu, Lei Zheng +4
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but…
Long Text Generation via Adversarial Training with Leaked Information
Jiaxian Guo, Sidi Lu, Han Cai +3
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining wit…