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Found 53 papers, 22 papers with code
Continuous Domain Generalization
Real-world data distributions often shift continuously across multiple latent factors such as time, geography, and socioeconomic context.
Connecting the Dots: A Chain-of-Collaboration Prompting Framework for LLM Agents
To address these challenges, we propose Cochain, a collaboration prompting framework that effectively solves business workflow collaboration problem by combining knowledge and prompts at a reduced cost.
Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting
To address this limitation, we reconsider the design of adaptive embeddings and propose a Principal Component Analysis (PCA) embedding approach that enables models to adapt to new scenarios without retraining.
Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting
Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squashing from high node resistance distances.
Cloth-Changing Person Re-Identification
Density Ratio Estimation
+1
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
The generalization ability of these models remains largely unexplored.
Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification
In this paper, we empirically and experimentally demonstrate that completely eliminating or fully retaining clothing features is detrimental to the task.
Robust Traffic Forecasting against Spatial Shift over Years
Through meticulous analysis, we attribute this decline to the models' inability to adapt to previously unobserved spatial relationships.
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
STGformer effectively balances the strengths of GCNs and Transformers, enabling efficient modeling of both global and local traffic patterns while maintaining a manageable computational footprint.
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process.
Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI Applications
We executed a series of neural decoding experiments involving nonhuman primates engaged in random reaching tasks, evaluating four prospective models, Gated Recurrent Unit (GRU), Transformer, Receptance Weighted Key Value (RWKV), and Selective State Space model (Mamba), across several metrics: single-session decoding, multi-session decoding, new session fine-tuning, inference speed, calibration speed, and scalability.
Continuous Temporal Domain Generalization
Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions.
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications.
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning
Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments.
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space.
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches.
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather.
Ranked #1 on
Traffic Prediction
on EXPY-TKY
(using extra training data)
Hyper-Relational Knowledge Graph Neural Network for Next POI
In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity. To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Ranked #1 on
Traffic Prediction
on Beijing Traffic
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution
Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes.
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.
Ranked #2 on
Traffic Prediction
on PeMSD7
Extend Wave Function Collapse to Large-Scale Content Generation
However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs.
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention
Recent years have witnessed the great potential of attention mechanism in graph representation learning.
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
Ranked #1 on
Time Series Forecasting
on Weather (96)
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data.
Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Ranked #2 on
Traffic Prediction
on EXPY-TKY
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances.
Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization
We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies.
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
TrafPS consists of three layers, from data process to results computation and visualization.
Event-Aware Multimodal Mobility Nowcasting
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.
Differentiable Projection for Constrained Deep Learning
In this paper, we propose to use a differentiable projection layer in DNN instead of directly solving time-consuming KKT conditions.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments.
Adaptive Q-learning for Interaction-Limited Reinforcement Learning
Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, i. e., a pessimistic update strategy for the offline dataset and a greedy or no pessimistic update scheme for the online dataset.
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning
URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.
An open GPS trajectory dataset and benchmark for travel mode detection
In this study, we propose and open GPS trajectory dataset marked with travel mode and benchmark for the travel mode detection.
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view.
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment.
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events
Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.
Visual Graph Mining
In this study, we formulate the concept of "mining maximal-size frequent subgraphs" in the challenging domain of visual data (images and videos).
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns
Graph matching and graph mining are two typical areas in artificial intelligence.
When 3D Reconstruction Meets Ubiquitous RGB-D Images
3D reconstruction from a single image is a classical problem in computer vision.
Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models
We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes.
