1 code implementation • 24 Feb 2024 • Hongyu Sun, Yongcai Wang, Wang Chen, Haoran Deng, Deying Li
Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding.
1 code implementation • 14 Feb 2024 • Linfeng Cao, Haoran Deng, Yang Yang, Chunping Wang, Lei Chen
In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally.
no code implementations • 20 Dec 2023 • Dafeng Zhu, Bo Yang, Yu Wu, Haoran Deng, ZhaoYang Dong, Kai Ma, Xinping Guan
This paper presents a carbon-energy coupling management framework for an industrial park, where the carbon flow model accompanying multi-energy flows is adopted to track and suppress carbon emissions on the user side.
no code implementations • 14 Aug 2023 • Haoran Deng, Bo Yang, Mo-Yuen Chow, Gang Yao, Cailian Chen, Xinping Guan
However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is also known as the ``chicken and egg'' conundrum.
1 code implementation • 15 Jun 2023 • Haoran Deng, Yang Yang, Jiahe Li, Haoyang Cai, ShiLiang Pu, Weihao Jiang
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice.
no code implementations • 4 Dec 2022 • Haoran Deng, Bo Yang, Chao Ning, Cailian Chen, Xinping Guan
In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network.
1 code implementation • 19 Oct 2022 • Amit Milstein, Haoran Deng, Guy Revach, Hai Morgenstern, Nir Shlezinger
In this work, we propose KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading.
no code implementations • 12 Apr 2020 • Jiancheng Yang, Haoran Deng, Xiaoyang Huang, Bingbing Ni, Yi Xu
In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules.
no code implementations • 13 Sep 2019 • Xiaoyang Huang, Jiancheng Yang, Linguo Li, Haoran Deng, Bingbing Ni, Yi Xu
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making.
no code implementations • 6 Aug 2019 • Yunxiang Zhang, Chenglong Zhao, Bingbing Ni, Jian Zhang, Haoran Deng
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning.