Multi-Pass Deep Q-Networks (MP-DQN) fixes the over-paramaterisation problem of P-DQN by splitting the action-parameter inputs to the Q-network using several passes (in a parallel batch). Split Deep Q-Networks (SP-DQN) is a much slower solution which uses multiple Q-networks with/withou...
(F), \label {eq: atention-transformer} (2) where F denotes the deep feature extracted from the input image I; ⊛ indicates the matrix multiplication; Q(·), K(·), and V(·) denote the operations that extract the matrices for the query, key, and value; ...
While deep learning (DL) based approaches13 have the ability to learn discriminative features, the heterogeneous appearance of nuclei in different cohorts and often the availability of only a small number of representative training samples may hinder these supervised learning based approaches. Thus, it...
This work provides a deep understanding of the plastic deformation process of Gr/MMCs and can be a guide for the preparation of Gr/MMCs with high performance. 2. Experimental Section 2.1. Synthesis of Graphene on Cu Particles via an In Situ Method Commercial flake Cu powders (purity of 99.9...
This is also in agreement with the higher ductility obtained in annealed samples, which is manifested in the fractographs through deep dimples. Naghizadeh and Mirzadeh [43] have also seen that on the fracture surfaces of AISI 304 stainless steel samples, size and depth of dimples increase with...
This is also in agreement with the higher ductility obtained in annealed samples, which is manifested in the fractographs through deep dimples. Naghizadeh and Mirzadeh [43] have also seen that on the fracture surfaces of AISI 304 stainless steel samples, size and depth of dimples increase with...