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Found 5 papers, 1 papers with code
Where2Start: Leveraging initial States for Robust and Sample-Efficient Reinforcement Learning
The strategy retrieves more informative trajectories, so the agent can learn with fewer trajectory sample.
Adaptive Low-Rank Regularization with Damping Sequences to Restrict Lazy Weights in Deep Networks
The experimental results show that ALR regularizes the deep networks well with high training speed and low resource usage.
Theory of adaptive SVD regularization for deep neural networks
With a different approach, this paper presents a regularization method based on the Singular Value Decomposition (SVD) that adjusts the learning model adaptively.
Adaptive Low-Rank Factorization to regularize shallow and deep neural networks
However, this scheme similar to [1] probably decreases the training accuracy when it tries to decrease the number of operations.
Regularized Deep Networks in Intelligent Transportation Systems: A Taxonomy and a Case Study
Both of these paradigms are utilized in the recent intelligent transportation systems (ITS) to support decision-making by the aid of different operations such as frequent patterns mining, regression, clustering, and classification.
