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07/29/2023
Continual Learning in Predictive Autoscaling
Predictive Autoscaling is used to forecast the workloads of servers and ...
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06/16/2023
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
Automatic bill payment is an important part of business operations in fi...
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05/01/2023
Full Scaling Automation for Sustainable Development of Green Data Centers
The rapid rise in cloud computing has resulted in an alarming increase i...
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11/21/2022
A Graph Regularized Point Process Model For Event Propagation Sequence
Point process is the dominant paradigm for modeling event sequences occu...
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10/06/2022
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning
Digital human recommendation system has been developed to help customers...
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07/11/2022
Learning Large-scale Universal User Representation with Sparse Mixture of Experts
Learning user sequence behaviour embedding is very sophisticated and cha...
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05/31/2022
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an imp...
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01/29/2022
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
We consider a sequential decision making problem where the agent faces t...
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05/09/2021
Unit Ball Model for Hierarchical Embeddings in Complex Hyperbolic Space
Learning the representation of data with hierarchical structures in the ...
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06/16/2020
Model Embedding Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has shown its advantages in sa...
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06/09/2020
Neural Physicist: Learning Physical Dynamics from Image Sequences
We present a novel architecture named Neural Physicist (NeurPhy) to lear...
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05/19/2020
Riemannian Proximal Policy Optimization
In this paper, We propose a general Riemannian proximal optimization alg...
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04/19/2020
Intention Propagation for Multi-agent Reinforcement Learning
A hallmark of an AI agent is to mimic human beings to understand and int...
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01/30/2018