activity
20192026
most citedNVIDIA FLARE: Federated Learning from Simulation to Real-World

60 citations · 65 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Version

Sean Bin Yang, Hao Miao, Zongyi Xu +5

Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation s…

cs.LG2025

MM-Path: Multi-modal, Multi-granularity Path Representation Learning -- Extended Version

Ronghui Xu, Hanyin Cheng, Chenjuan Guo +4

Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learni…

cs.LG2023★ 2 cited

LightPath: Lightweight and Scalable Path Representation Learning

Sean Bin Yang, Jilin Hu, Chenjuan Guo +2

Movement paths are used widely in intelligent transportation and smart city applications. To serve such applications, path representation learning aims to provide compact represent…

cs.LG2022★ 60 cited

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Holger R. Roth, Yan Cheng, Yuhong Wen +20

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVI…

cs.LG2022★ 1 cited

Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version

Sean Bin Yang, Chenjuan Guo, Jilin Hu +3

In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A…

cs.LG2021★ 2 cited

Unsupervised Path Representation Learning with Curriculum Negative Sampling

Sean Bin Yang, Chenjuan Guo, Jilin Hu +2

Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in nav…

cs.LG2019

PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks

Sean Bin Yang, Bin Yang

Modern navigation services often provide multiple paths connecting the same source and destination for users to select. Hence, ranking such paths becomes increasingly important, wh…