Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography.
This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms.
Its feasibility has been demonstrated with numerical simulations of the adiabatic preparation for certain incommensurate particle-doping fractions, where the major problem to circumvent is the atomic localization in the incommensurate lattice.
Quantum Gases Strongly Correlated Electrons Quantum Physics
Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states.
In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation.
We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability.
The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection.
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.
We use Chow-Liu's algorithm to learn a tree-structured probabilistic model for the units at the current level, use the tree to identify subsets of units that are strongly correlated, and introduce a new unit with receptive field over the subsets.
Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively.