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Found 5 papers, 3 papers with code
Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection
Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models.
DataLight: Offline Data-Driven Traffic Signal Control
Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges.
DynamicLight: Two-Stage Dynamic Traffic Signal Timing
Reinforcement learning (RL) is gaining popularity as an effective approach for traffic signal control (TSC) and is increasingly applied in this domain.
DynLight: Realize dynamic phase duration with multi-level traffic signal control
We would like to withdraw this article for the following reasons: 1 this article is not satisfactory for limited language and theoretical description; 2 we have enriched and revised this article with the help of other authors; 3 we must update the author contribution information.
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization
We propose two new methods: (1) Max Queue-Length (M-QL), an optimization-based traditional method designed based on the property of queue length; and (2) AttentionLight, an RL model that employs the self-attention mechanism to capture the signal phase correlation without requiring human knowledge of phase relationships.
