FontRNN: Generating Large‐scale Chinese Fonts via Recurrent Neural Network

2019 ◽  
Vol 38 (7) ◽  
pp. 567-577 ◽  
Author(s):  
Shusen Tang ◽  
Zeqing Xia ◽  
Zhouhui Lian ◽  
Yingmin Tang ◽  
Jianguo Xiao
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale

Large scale recurrent neural network on GPU

2014 ◽  
Author(s):  
Boxun Li ◽  
Erjin Zhou ◽  
Bo Huang ◽  
Jiayi Duan ◽  
Yu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale

scholarly journals Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Sudip Mandal ◽  
Abhinandan Khan ◽  
Goutam Saha ◽  
Rajat K. Pal
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Real World ◽  
Large Scale ◽  
Cuckoo Search ◽  
Genetic Networks ◽  
Computational Time ◽  
Flower Pollination Algorithm ◽  
Small Scale ◽  
Flower Pollination

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

scholarly journals Inferring brain-wide interactions using data-constrained recurrent neural network models

2020 ◽  
Author(s):  
Matthew G. Perich ◽  
Charlotte Arlt ◽  
Sofia Soares ◽  
Megan E. Young ◽  
Clayton P. Mosher ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Computational Models ◽  
Network Models ◽  
Brain Regions ◽  
Neural Network Models ◽  
Neural Data ◽  
Neural Recordings ◽  
Using Data

ABSTRACTBehavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

scholarly journals Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

2019 ◽  
Vol 33 ◽  
pp. 3656-3663 ◽  
Author(s):  
Xu Geng ◽  
Yaguang Li ◽  
Leye Wang ◽  
Lingyu Zhang ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Traffic Congestion ◽  
Large Scale ◽  
Contextual Information ◽  
Demand Forecasting ◽  
Wait Time ◽  
Temporal Correlation ◽  
Gating Mechanism ◽  
Proposed Model

Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regions. Existing approaches mainly focus on modeling the Euclidean correlations among spatially adjacent regions while we observe that non-Euclidean pair-wise correlations among possibly distant regions are also critical for accurate forecasting. In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting. We first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi-graph convolution. To utilize the global contextual information in modeling the temporal correlation, we further propose contextual gated recurrent neural network which augments recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. We evaluate the proposed model on two real-world large scale ride-hailing demand datasets and observe consistent improvement of more than 10% over stateof-the-art baselines.

scholarly journals Reinforcement learning in a large-scale photonic recurrent neural network

Optica ◽  
2018 ◽  
Vol 5 (6) ◽  
pp. 756 ◽  
Author(s):  
J. Bueno ◽  
S. Maktoobi ◽  
L. Froehly ◽  
I. Fischer ◽  
M. Jacquot ◽  
...  
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Recurrent Neural Network ◽  
Large Scale

scholarly journals A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2737
Author(s):  
Yizhen Wang ◽  
Ningqing Zhang ◽  
Xiong Chen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Power Load ◽  
Power Load Forecasting

With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.

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