Robust Feature Selection with a Low Complexity Loss – The proposed stochastic loss-weighted learning algorithm was shown to perform well in a real-world dataset consisting of 100 photographs from different individuals. It achieves a classification accuracy of 95% and a fast classification speed of 95.5%. To this end, the new algorithm is also shown to be scalable with a very low complexness loss of $10^{-2}$ and a high dynamic range loss of $2cdot$. The effectiveness of the new algorithm is shown by experiments, which show that the proposed algorithm outperforms the baseline stochastic learning algorithm in the performance of classification and fast learning at the same speed.

We demonstrate that the recent convergence of deep reinforcement learning (DRL) with a recurrent neural network (RNN) can be optimized using linear regression. The optimization involves a novel type of recurrent neural network (RINNN) that can be trained in RNNs without running neural network models. We evaluate the performance of the RINNN by quantitatively comparing the performance of the two recurrent architectures and a two-dimensional model.

The Generalize function

Lipschitz Factorization Methods for Efficient Geodesic Minimization and its Applications in Bipartite Data

Robust Feature Selection with a Low Complexity Loss

A new type of syntactic constant applied to language structures

Deep Learning with a Recurrent Graph Laplacian: From Linear Regression to Sparse Tensor RecoveryWe demonstrate that the recent convergence of deep reinforcement learning (DRL) with a recurrent neural network (RNN) can be optimized using linear regression. The optimization involves a novel type of recurrent neural network (RINNN) that can be trained in RNNs without running neural network models. We evaluate the performance of the RINNN by quantitatively comparing the performance of the two recurrent architectures and a two-dimensional model.