Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Lu, Xiuyuan"'
Event-based visual odometry is a specific branch of visual Simultaneous Localization and Mapping (SLAM) techniques, which aims at solving tracking and mapping sub-problems in parallel by exploiting the special working principles of neuromorphic (ie,
Externí odkaz:
http://arxiv.org/abs/2410.09374
Autor:
Jin, Wangxiao, He, Siyu, Lu, Xiuyuan, Zhu, Xitong, Liu, Dijiong, Sun, Guolong, Hao, Yanlei, Yan, Xiaolin, Yan, Yiran, Wu, Longjia, Lin, Xiongfeng, Hou, Wenjun, Cao, Weiran, Liu, Chuan, Liang, Xiaoci, Gao, Yuan, Deng, Yunzhou, Gao, Feng, Jin, Yizheng
Solution-processed light-emitting diodes (LEDs) are appealing for their potential in the low-cost fabrication of large-area devices. However, the limited performance of solution-processed blue LEDs, particularly their short operation lifetime, is hin
Externí odkaz:
http://arxiv.org/abs/2409.04283
Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system. One bottleneck of existing vision-based solutions is that their updating rate is limited to the frame rate of standard cam
Externí odkaz:
http://arxiv.org/abs/2407.07324
Existing algorithms for reinforcement learning from human feedback (RLHF) can incentivize responses at odds with preferences because they are based on models that assume independence of irrelevant alternatives (IIA). The perverse incentives induced b
Externí odkaz:
http://arxiv.org/abs/2312.01057
Neuromorphic event-based cameras are bio-inspired visual sensors with asynchronous pixels and extremely high temporal resolution. Such favorable properties make them an excellent choice for solving state estimation tasks under aggressive ego motion.
Externí odkaz:
http://arxiv.org/abs/2311.18189
Autor:
Osband, Ian, Wen, Zheng, Asghari, Seyed Mohammad, Dwaracherla, Vikranth, Ibrahimi, Morteza, Lu, Xiuyuan, Van Roy, Benjamin
Thompson sampling (TS) is a popular heuristic for action selection, but it requires sampling from a posterior distribution. Unfortunately, this can become computationally intractable in complex environments, such as those modeled using neural network
Externí odkaz:
http://arxiv.org/abs/2302.09205
Autor:
Lu, Xiuyuan, Osband, Ian, Asghari, Seyed Mohammad, Gowal, Sven, Dwaracherla, Vikranth, Wen, Zheng, Van Roy, Benjamin
Recent work introduced the epinet as a new approach to uncertainty modeling in deep learning. An epinet is a small neural network added to traditional neural networks, which, together, can produce predictive distributions. In particular, using an epi
Externí odkaz:
http://arxiv.org/abs/2207.00137
Autor:
Dwaracherla, Vikranth, Wen, Zheng, Osband, Ian, Lu, Xiuyuan, Asghari, Seyed Mohammad, Van Roy, Benjamin
In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains an ensemble of models. In recent years, several approaches have been p
Externí odkaz:
http://arxiv.org/abs/2206.03633
Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a regret bound that ensures desirable behavi
Externí odkaz:
http://arxiv.org/abs/2203.01303