10 Result(s)
within
Computer Science
ChunLin Chen
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Article
Sparse spatial transformers for few-shot learning
Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose useful local information. Many f...
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Article
Rolling horizon wind-thermal unit commitment optimization based on deep reinforcement learning
The growing penetration of renewable energy has brought significant challenges for modern power system operation. Academic research and industrial practice show that adjusting unit commitment (UC) scheduling p...
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Chapter and Conference Paper
Multi-level Metric Learning for Few-Shot Image Recognition
Few-shot learning devotes to training a model on a few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, using global features may lose loc...
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Chapter and Conference Paper
Local Mutual Metric Network for Few-Shot Image Classification
Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches tend to represent each sample with a high-level semantic representatio...
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Article
Hybrid MDP based integrated hierarchical Q-learning
As a widely used reinforcement learning method, Q-learning is bedeviled by the curse of dimensionality: The computational complexity grows dramatically with the size of state-action space. To combat this diffi...
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Chapter and Conference Paper
Two-Level Verification of Data Integrity for Data Storage in Cloud Computing
Data storage in cloud computing can save capital expenditure and relive burden of storage management for users. As the lose or corruption of files stored may happen, many researchers focus on the verification ...
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Chapter and Conference Paper
Scheduling Active Services in Clustered JBI Environment
Active services may cause business or runtime errors in clustered JBI environment. To cope with this problem, a scheduling mechanism is proposed. The overall scheduling framework and scheduling algorithm is gi...
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Chapter and Conference Paper
Grey Reinforcement Learning for Incomplete Information Processing
New representation and computation mechanisms are key approaches for learning problems with incomplete information or in large probabilistic environments. In this paper, traditional reinforcement learning (RL)...
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Chapter and Conference Paper
Quantum Reinforcement Learning
A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introdu...
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Chapter and Conference Paper
An Autonomous Mobile Robot Based on Quantum Algorithm
In this paper, we design a novel autonomous mobile robot which uses quantum sensors to detect faint signals and fulfills some learning tasks using quantum reinforcement learning (QRL) algorithms. In this robot...
