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Found 32 papers, 14 papers with code
NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification
This report provide a detailed description of the method that we proposed in the TRAC-2024 Offline Harm Potential dentification which encloses two sub-tasks.
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning
Developing effective Map Entity Representation Learning (MERL) methods is crucial to extracting embedding information from electronic maps and converting map entities into representation vectors for downstream applications.
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions.
Full Bayesian Significance Testing for Neural Networks
Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations.
Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
It first encodes traffic data into two disentangled representations for associating invariant and variant ST contexts.
Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning
The framework consists of two main components: an automatic graph decomposition module that decomposes the original graph structure inherent in ST data into subgraphs corresponding to different factors, and a decomposed learning network that learns the partial ST data on each subgraph separately and integrates them for the final prediction.
Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]
The field of urban spatial-temporal prediction is advancing rapidly with the development of deep learning techniques and the availability of large-scale datasets.
Statistical Tests for Replacing Human Decision Makers with Algorithms
This paper proposes a statistical framework with which artificial intelligence can improve human decision making.
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs.
The Web Can Be Your Oyster for Improving Large Language Models
In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search engine.
LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems.
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense
While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks.
BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022
Therefore, organizers provide a wind power dataset containing historical data from 134 wind turbines and launch the Baidu KDD Cup 2022 to examine the limitations of current methods for wind power forecasting.
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems.
Ranked #2 on
Traffic Prediction
on PeMSD4
Continuous Trajectory Generation Based on Two-Stage GAN
Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect.
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods.
Ranked #1 on
Traffic Prediction
on BJTaxi
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation.
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities.
Physics-informed machine learning
Spatio-Temporal Forecasting
+1
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM).
LibCity: An Open Library for Traffic Prediction
This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework.
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States
We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobility, and unemployment.
Deep Sequence Modeling: Development and Applications in Asset Pricing
We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling.
Curriculum Pre-Training Heterogeneous Subgraph Transformer for Top-$N$ Recommendation
Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in top-$N$ recommender systems, called \emph{HIN-based recommendation}.
Interpreting Deep Learning Model Using Rule-based Method
For global explanation, frequency-based and out-of-bag based methods are proposed to extract important features in the neural network decision.
Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States
Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).
Are L2 adversarial examples intrinsically different?
That is, adversarial examples generated by $L_2$ attacks usually have larger input sensitivity which can be used to identify them efficiently.
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks
Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT).
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation
To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.
Decision Making with Machine Learning and ROC Curves
Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.
Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization
In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data.
SVM-based Deep Stacking Networks
The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning.
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis
In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis.
