Video coding standards use a prediction structure to arrange video frames and exploit temporal correlations. In this aspect, it is crucial resolve complicated dependencies among improve efficiency because the of preceding frame affects rate-distortion (R-D) performance subsequent frames. Previous algorithms have attempted address problem using handcrafted features or analytical models even thou...

Abstract Rate distortion theory is concerned with optimally encoding signals from a given signal class $$\mathcal {S}$$ S using budget of R bits, as $$R \rightarrow \infty $$ R → ∞ . We say that can be compressed at rate s if we achieve...

In the paper we study a deep learning based method to solve multicell downlink power control problem for sum rate maximization subject per-user constraints and per-base station (BS) constraints. The core difficulty of this is how ensure that learned results by neural network (DNN) satisfy To tackle difficulty, propose cascade projection block after traditional DNN, which projects infeasible ont...

One of the most important hyper-parameters for model training and generalization is learning rate. Recently, many research studies have shown that optimizing rate schedule very useful deep neural networks to get accurate efficient results. In this paper, different schedules using some comprehensive optimization techniques been compared in order measure accuracy a convolutional network CNN class...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...