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Found 12 papers, 4 papers with code
RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer
However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories.
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing
We introduce structural semantic metrics based on graph adjacency relations during the semantic reconstruction and correlation mining stage and meanwhile align the structure semantics in the hash space with an asymmetric binary optimization process.
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Self-Guided Learning to Denoise for Robust Recommendation
Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness.
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns.
Ranked #1 on
Recommendation Systems
on Alibaba-iFashion
Crowding Prediction of In-Situ Metro Passengers Using Smart Card Data
The metro system is playing an increasingly important role in the urban public transit network, transferring a massive human flow across space everyday in the city.
Probabilistic Value Selection for Space Efficient Model
Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy.
TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data
It strategically decouples these two inferencetasks by only taking those trip records with only one practical route as the input for the first inference taskof travel time and feeding the inferred travel time to the second inference task as an additional input whichnot only improves the accuracy but also effectively reduces the complexity of both inference tasks.
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification
We propose Crowd-based neural networks for Text Sentiment Classification (CrowdTSC for short).
DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.
Ineffectiveness of Dictionary Coding to Infer Predictability Limits of Human Mobility
Consequently, it is ineffective to infer predictability through dictionary coding algorithms.
DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision
Estimating the travel time of a path is of great importance to smart urban mobility.
