The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribution. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech translation or image captioning have so far ignored the geometry of the late...

A widely studied non-deterministic polynomial time (NP) hard problem lies in €nding a route between the two nodes of a graph. O‰en meta-heuristics algorithms such asA∗ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text translation. Particularly, we ...

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important generalpurpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work, we introduce a model and beamsearch training scheme, based on the work of Da...

Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics. This paper is the first step towards this goal; we propose a proofsynthesis method for the negation-free propositional logic in which we use a DNN to obtain a guide of proof search. The idea is to view the proof-synthesis problem as a translation from a proposi...

With the advent of big data, easy-to-get GPGPU and progresses in neural network modeling techniques, training deep learning model on GPU becomes a popular choice. However, due to the inherent complexity of deep learning models and the limited memory resources on modern GPUs, training deep models is still a nontrivial task, especially when the model size is too big for a single GPU. In this pape...

Today, the use of the Internet and Internet sites has been an integrated part of the people’s lives, and most activities and important data are in the Internet websites. Thus, attempts to intrude into these websites have grown exponentially. Intrusion detection systems (IDS) of web attacks are an approach to protect users. But, these systems are suffering from such drawbacks as low accuracy in ...

Power load forecasting has gained considerable research interest in recent years. The power is vulnerable to randomness and uncertainty during grid operations. Therefore, it crucial effectively predict the electric improve accuracy of prediction. This study proposes a novel method based on an improved long short-term memory (LSTM) neural network. Thus, network model established for forecasting,...

Convolutional neural networks (CNN) have recently achieved remarkable performance in a wide range of applications. In this research, we equip convolutional sequence-to-sequence (seq2seq) model with an efficient graph linearization technique for abstract meaning representation parsing. Our linearization method is better than the prior method at signaling the turn of graph traveling. Additionally...

A widely studied non-polynomial (NP) hard problem lies in finding a route between the two nodes of a graph. Often meta-heuristics algorithms such asA∗ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Sequence model, widely used, for instance in text translation. Particularly, we illustrate that utilising a...