Results 71 to 80 of about 21,033 (209)
Image Retrieval and Pattern Spotting using Siamese Neural Network [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 This paper presents a novel approach for image retrieval and pattern spotting in document image collections. The manual feature engineering is avoided by learning a similarity-based representation using a Siamese Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset.
Wiggers, Kelly L. +4 more
openaire +3 more sources
Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
Journal of Forecasting, EarlyView.ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer +3 more
wiley +1 more source
Deep template matching for offline handwritten Chinese character recognition
The Journal of Engineering, 2019 Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success.
Zhiyuan Li +4 more
doaj +1 more source
Semantic Textual Similarity with Siamese Neural Networks
Proceedings - Natural Language Processing in a Deep Learning World, 2019 Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.
Ranasinghe, Tharindu +2 more
openaire +1 more source
INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK [PDF]
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost
L. Chen, F. Rottensteiner, C. Heipke
openaire +3 more sources
A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios
Journal of Forecasting, EarlyView.ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley +1 more source
Journal of Forecasting, EarlyView.ABSTRACT Food waste reveals inefficiencies in resource use and contributes significantly to environmental pollution. In the United States, food waste accounts for the majority of total waste, with more than 50 million tons generated annually. In military units that operate large‐scale institutional food‐service facilities and generate significant food ...
YongSun Kim, Hyun Shik Yoon
wiley +1 more source
Deep Spatial-Temporal Joint Feature Representation for Video Object Detection
Sensors, 2018 With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection
Baojun Zhao +4 more
doaj +1 more source
Performance Evaluation of the MPAS Model in Simulating Southeast Asian Rainfall Characteristics
International Journal of Climatology, EarlyView.This study evaluates the performance of the Model for Prediction Across Scales–Atmosphere (MPAS) in reproducing key rainfall characteristics over Southeast Asia (SEA) during 2000–2020, using the MSWEP dataset as reference. MPAS realistically captures the observed meridional rainfall gradient, with higher rainfall in the south and lower in the north, as
Nguyen Thanh Hung +4 more
wiley +1 more source
IEEE Access, 2019 In this paper, we present a novel convolutional neural network (CNN)-based model for change detection in synthetic aperture radar (SAR) images. Considering that change detection task takes image pairs as an input, we first explore multiple neural network
Huihui Dong +4 more
doaj +1 more source