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Found 5 papers, 1 papers with code
Deep Explainable Learning with Graph Based Data Assessing and Rule Reasoning
Learning an explainable classifier often results in low accuracy model or ends up with a huge rule set, while learning a deep model is usually more capable of handling noisy data at scale, but with the cost of hard to explain the result and weak at generalization.
Event-triggered Consensus of Matrix-weighted Networks Subject to Actuator Saturation
The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy.
Intelligent Trainer for Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical reinforcement learning (RL), by leveraging a learned model to generate synthesized data for policy training purpose.
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning
Specifically, we propose an end-to-end cooling control algorithm (CCA) that is based on the actor-critic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm.
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM
Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%.
