30 citations · 43 across the 8 of their papers we have counts for
7 papers · 1 filter
Trajectory Flow Map: Graph-based Approach to Analysing Temporal Evolution of Aggregated Traffic Flows in Large-scale Urban Networks
Jiwon Kim, Kai Zheng, Jonathan Corcoran +2
This paper proposes a graph-based approach to representing spatio-temporal trajectory data that allows an effective visualization and characterization of city-wide traffic dynamics…
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee +5
Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. C…
Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction
Seongjin Choi, Jiwon Kim, Hwasoo Yeo
With the increasing deployment of diverse positioning devices and location-based services, a huge amount of spatial and temporal information has been collected and accumulated as t…
Doubly Nested Network for Resource-Efficient Inference
Jaehong Kim, Sungeun Hong, Yongseok Choi +1
We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While…
Meta Continual Learning
Risto Vuorio, Dong-Yeon Cho, Daejoong Kim +1
Using neural networks in practical settings would benefit from the ability of the networks to learn new tasks throughout their lifetimes without forgetting the previous tasks. This…
Auto-Meta: Automated Gradient Based Meta Learner Search
Jaehong Kim, Sangyeul Lee, Sungwan Kim +6
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and tim…