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Found 28 papers, 5 papers with code
REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories
Location prediction forecasts a user's location based on historical user mobility traces.
Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows
Due to non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting.
Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation
In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual needs and preferences.
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
We design several experiments to indicate that our framework can outperform compared to other generative models for the urban planning task.
Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
Subsequently, we utilize the defined query-aware temporal paths on a history temporal graph to model historical path information related to queries for reasoning.
Deep Generative Imputation Model for Missing Not At Random Data
Existing statistical methods model the MNAR mechanism by different decomposition of the joint distribution of the complete data and the missing mask.
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications.
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently.
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models.
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning
To tackle the challenges, we formulate the problem as a course representation learning task-based and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation.
Toward Efficient Automated Feature Engineering
Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy.
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations.
Graph Soft-Contrastive Learning via Neighborhood Ranking
Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency.
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.
Semi-supervised Drifted Stream Learning with Short Lookback
In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i. i. d.
Reinforced Imitative Graph Learning for Mobile User Profiling
The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities.
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.
Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation
However, three major challenges arise: 1) how to define a quantitative land-use configuration?
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT.
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models.
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task.
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective
To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method.
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection
To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations.
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area.
