In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, P...

This paper analyzes the problem of learning the structure of a Bayes net (BN) in the theoretical framework of Gold’s learning paradigm. Bayes nets are one of the most prominent formalisms for knowledge representation and probabilistic and causal reasoning. We follow constraint-based approaches to learning Bayes net structure, where learning is based on observed conditional dependencies between ...

Textbooks play a crucial role in English language teaching and learning. One of the methods for selecting the most appropriate textbook for a particular educational purpose is comparing two or several similar textbooks with each other. Textbook evaluation can provide useful information about the strengths and weaknesses of textbooks and can lead to the selection of the most suitable materials f...

In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net the visual content of images and image acquisition artifacts, JPEG artifact module compression artifacts in discrete cosine transform (DCT) domain. The backbone model based on pre-training, such as CAT-Net, representative detection, has...

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...