1 code implementation • 30 Jan 2024 • Weijia Zhang, Jindong Han, Zhao Xu, Hang Ni, Hao liu, Hui Xiong
Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments.
1 code implementation • 26 Dec 2023 • Siqi Lai, Zhao Xu, Weijia Zhang, Hao liu, Hui Xiong
Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion.
1 code implementation • 24 Dec 2023 • Weijia Zhang, Chun Kai Ling, Xuanhui Zhang
Censoring is the central problem in survival analysis where either the time-to-event (for instance, death), or the time-tocensoring (such as loss of follow-up) is observed for each sample.
1 code implementation • 28 Oct 2023 • Weijia Zhang, Dongnan Liu, Chao Ma, Weidong Cai
Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in autonomous driving due to absence of explicit depth cues in a single RGB image.
1 code implementation • 27 Oct 2023 • Wei Fan, Weijia Zhang, Weiqi Wang, Yangqiu Song, Hao liu
Conversational Recommender Systems (CRS) illuminate user preferences via multi-round interactive dialogues, ultimately navigating towards precise and satisfactory recommendations.
no code implementations • 14 Oct 2023 • Jindong Han, Weijia Zhang, Hao liu, Hui Xiong
In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting.
no code implementations • 31 Aug 2023 • Weijia Zhang, Le Zhang, Jindong Han, Hao liu, Jingbo Zhou, Yu Mei, Hui Xiong
Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.
1 code implementation • 26 Jun 2023 • Siqi Lai, Weijia Zhang, Hao liu
To this end, this paper proposes a preference-aware meta-optimization framework Meta-Pec for personalized vehicle energy consumption estimation.
1 code implementation • 25 Jun 2023 • Fan Liu, Weijia Zhang, Hao liu
Therefore, improving the adversarial robustness of these models is crucial for ITS.
no code implementations • 19 Jun 2023 • Feiyu Chen, Haiping Ma, Weijia Zhang
To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model.
no code implementations • 10 May 2023 • Hanwen Deng, Weijia Zhang, Min-Ling Zhang
Label noise widely exists in large-scale datasets and significantly degenerates the performances of deep learning algorithms.
no code implementations • 27 Mar 2023 • Zhaofei Wang, Weijia Zhang, Min-Ling Zhang
However, since such approaches only utilize the highest score proposal and discard the potentially useful information from other proposals, their independent MIL backbone often limits models to salient parts of an object or causes them to detect only one object per class.
1 code implementation • 18 Dec 2022 • Wei Tang, Weijia Zhang, Min-Ling Zhang
MIPLGP first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes.
1 code implementation • 25 Feb 2022 • Weijia Zhang, Xuanhui Zhang, Han-Wen Deng, Min-Ling Zhang
Multi-instance learning (MIL) deals with objects represented as bags of instances and can predict instance labels from bag-level supervision.
Multiple Instance Learning Out-of-Distribution Generalization +1
no code implementations • 14 Dec 2021 • Weijia Zhang, Svitlana Vakulenko, Thilina Rajapakse, Yumo Xu, Evangelos Kanoulas
In this dataset, answering the query requires document retrieval from a knowledge corpus.
1 code implementation • IJCAI 2021 • Weijia Zhang
Multi-instance learning is a type of weakly supervised learning.
no code implementations • 12 Jul 2021 • Weijia Zhang, Hao liu, Lijun Zha, HengShu Zhu, Ji Liu, Dejing Dou, Hui Xiong
Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).
1 code implementation • 4 May 2021 • Weijia Zhang
Multi-instance learning is a type of weakly supervised learning.
1 code implementation • 15 Feb 2021 • Weijia Zhang, Hao liu, Fan Wang, Tong Xu, Haoran Xin, Dejing Dou, Hui Xiong
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 14 Jul 2020 • Weijia Zhang, Jiuyong Li, Lin Liu
A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a. k. a treatment effect).
no code implementations • 25 Mar 2020 • Jiuyong Li, Weijia Zhang, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu
We also propose a general framework for causal classification, by using off-the-shelf supervised methods for flexible implementations.
2 code implementations • 29 Jan 2020 • Weijia Zhang, Lin Liu, Jiuyong Li
Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i. e., variables that affect both the treatment and the outcome.
1 code implementation • 24 Nov 2019 • Weijia Zhang, Hao liu, Yanchi Liu, Jingbo Zhou, Hui Xiong
However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).
no code implementations • IJCNLP 2019 • Zenan Xu, Qinliang Su, Xiaojun Quan, Weijia Zhang
Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations.
no code implementations • 13 Feb 2019 • Weijia Zhang, Jiuyong Li, Lin Liu
Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances.