no code implementations • 25 May 2024 • Xuesong Wang, He Zhao, Edwin V. Bonilla
Neural Processes (NPs) are variational frameworks that aim to represent stochastic processes with deep neural networks.
no code implementations • 3 Apr 2024 • Xuesong Wang, Nina Fatehi, Caisheng Wang, Masoud H. Nazari
This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory.
no code implementations • 27 Mar 2024 • Mingxing Peng, Xusen Guo, Xianda Chen, Meixin Zhu, Kehua Chen, Hao, Yang, Xuesong Wang, Yinhai Wang
To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior.
no code implementations • 14 Jan 2024 • Xuesong Wang
To reduce the labeling cost and enhance the sample efficiency, Active Learning (AL) technique can be used to label as few samples as possible to reach a reasonable or similar results.
no code implementations • 20 Dec 2023 • Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai, Daocheng Fu, Tianqi Wang, Zhiyong Cui, Haiyang Yu, Xuesong Wang, Hanchu Zhou, Helai Huang, Yinhai Wang
For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction.
no code implementations • 24 Oct 2023 • Di Chen, Meixin Zhu, Hao Yang, Xuesong Wang, Yinhai Wang
The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field.
no code implementations • 15 Oct 2023 • Xuesong Wang, Yi Zhou, Dongsheng Zhang
With the increasing demand for high speed and high precision machining of machine tools, the problem of which factors of feed system ultimately determine the performance of machine tools is becoming more and more prominent.
no code implementations • 4 Aug 2023 • Dongsheng Zhang, Xuesong Wang, Tingting Zhang
Currently, the influence of changes in control parameters on the matching characteristics of each subsystem was not yet considered when studying the coupling relationship between subsystems.
no code implementations • 7 Jul 2023 • Xuesong Wang, Dongsheng Zhang, Zheng Zhang
With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand.
no code implementations • 12 Jun 2023 • Yining Ma, Wei Jiang, Lingtong Zhang, Junyi Chen, Hong Wang, Chen Lv, Xuesong Wang, Lu Xiong
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
1 code implementation • 1 Sep 2022 • Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yao
We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.
no code implementations • 2 Aug 2022 • Fanqi Meng, Xuesong Wang, Jingdong Wang, Peifang Wang
The innovation is that when categorizing bug reports, in addition to using the text information of the report, the intention of the report (i. e. suggestion or explanation) is also considered, thereby improving the performance of the classification.
no code implementations • 26 May 2022 • Wei Dai, Chuanfeng Ning, Shiyu Pei, Song Zhu, Xuesong Wang
As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning.
1 code implementation • 26 Mar 2022 • Xuesong Wang, Lina Yao, Islem Rekik, Yu Zhang
Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.
no code implementations • 4 Feb 2022 • Meixin Zhu, Simon S. Du, Xuesong Wang, Hao, Yang, Ziyuan Pu, Yinhai Wang
Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.
1 code implementation • 5 Dec 2021 • Xuesong Wang, Zhihang Hu, Tingyang Yu, Ruijie Wang, Yumeng Wei, Juan Shu, Jianzhu Ma, Yu Li
Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward.
1 code implementation • 2 Sep 2021 • Xuesong Wang, Lina Yao, Xianzhi Wang, Hye-Young Paik, Sen Wang
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
no code implementations • 1 Jan 2021 • Xuesong Wang, Caisheng Wang
Specifically, the input of the module will be resized into different scales on which position and semantic information will be processed, and then they will be rescaled back and combined with the module input.
no code implementations • 15 Jun 2020 • Xuesong Wang, Lina Yao, Xianzhi Wang, Feiping Nie
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
1 code implementation • 29 Jan 2019 • Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).
no code implementations • 3 Jan 2019 • Meixin Zhu, Xuesong Wang, Yinhai Wang
This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.