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Academic Articles & Research Papers
Showing 20 of 58 academic articles
Raban Iten, Tony Metger, Henrik Wilming, Lídia del Rio et al. • Physical Review Letters
(2020)
Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture afte...
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Open Access
DOI: https://doi.org/10.1103/physrevlett.124.010508
Christos Christodoulou, Michael Georgiopoulos • CERN Document Server (European Organization for Nuclear Research)
(2001)
From the Book:
Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...
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Aditya Malte, Pratik Ratadiya • arXiv (Cornell University)
(2019)
In this paper, we present a study of the recent advancements which have helped bring Transfer Learning to NLP through the use of semi-supervised training. We discuss cutting-edge methods and architectures such as BERT, GPT, ELMo, ULMFit amo...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1910.07370
Guoyu Zuo, Minghui Zhang, Jingwei Ren, Qifei Wu et al. • CAAI Transactions on Intelligence Technology
(2026)
ABSTRACT Knee joint moment estimation is a critical component in biomechanical analysis, with profound implications for rehabilitation assessment and the development of assistive devices like exoskeletons. However, existing data‐driven appr...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/cit2.70165
Naima Jawhar, Adnane Talha, Anas Bouayad • Discover Artificial Intelligence
(2026)
Abstract Task scheduling remains a challenge in IoT-Fog-Cloud systems due to dynamic workloads and heterogeneous resources. Ineffective scheduling strategies increase latency, energy consumption, and Service Level Agreement (SLA) violations...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-026-02614-0
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen et al. • arXiv (Cornell University)
(2011)
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1103.0398
CollobertRonan, WestonJason, BottouLéon, KarlenMichael et al. • Journal of Machine Learning Research
(2011)
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity rec......
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DOI: https://doi.org/10.5555/1953048.2078186
Ronan Collobert, Jason Weston • International Conference on Machine Learning
(2008)
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the lik...
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DOI: https://doi.org/10.1145/1390156.1390177
Irina V. Arinicheva, Igor V. Arinichev, Galina V. Volkova, Sergey V. Polyanskikh • Siberian Journal of Life Sciences and Agriculture
(2022)
Purpose. To investigate the possibility of diagnosing yellow and brown rust (Puccinia striiformis f. sp. tritici West. Puccinia triticina f. sp. tritici Erikss), yellow spot (Pyrenophora tritici-repentis (Died.) Drechsler) on wheat leaves f...
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Open Access (DOAJ)
DOI: 10.12731/2658-6649-2022-14-1-248-261
Muhammad Faseeh, Misbah Bibi, Murad Ali Khan, Atif Rizwan et al. • IEEE Access
(2024)
Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for ...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3485089
D. Zhong, D. Zhong, L. He, L. He et al. • ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
(2023)
Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The devel...
DOAJ
Open Access (DOAJ)
DOI: 10.5194/isprs-annals-X-1-W1-2023-295-2023
Hairui Zhu, Shanhong Guo, Weixing Sheng, Lei Xiao • Remote Sensing
(2023)
Ship classification technology using synthetic aperture radar (SAR) has become a research hotspot. Many deep-learning-based methods have been proposed with handcrafted models or using transplanted computer vision networks. However, most of ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs15112904
Xiaolong Ma, Peishun Liu, Haojie Gao, Zikang Yan et al. • IEEE Access
(2025)
In the fields of natural language processing and computer vision, the emergence of large pre-trained models has led to the adoption of fine-tuning them for downstream tasks as an important paradigm. However, the full fine-tuning approach of...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3529807
Nikita Andriyanov • Mathematics
(2022)
On the one hand, the solution of computer vision tasks is associated with the development of various kinds of images or random fields mathematical models, i.e., algorithms, that are called traditional image processing. On the other hand, no...
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Open Access (DOAJ)
DOI: 10.3390/math10214021
Yong Xu, Chaoda Zheng, Ruotao Xu, Yuhui Quan • IEEE Access
(2019)
Recognition of 3D shapes is a fundamental task in computer vision. In recent years, view-based deep learning has emerged as an effective approach for 3D shape recognition. Most existing view-based methods treat the views of an object as an ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2019.2934650
Tryan Aditya Putra, Syahidah Izza Rufaida, Jenq-Shiou Leu • IEEE Access
(2020)
In recent years, deep learning has taken the spotlight in automated medical bioimaging. However, the performance of current state-of-the-art score stems primarily from well-tuned parameters and architecture. There is still only limited rese...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2020.2976045
Juhyeon Kim, Emin Orhan, Kijung Yoon, Xaq Pitkow • IEEE Access
(2022)
Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2022.3178951
Nachimuthu Karunanithi, William J. Grenney, Darrell Whitley, Ken D. Bovee • Journal of Computing in Civil Engineering
(1994)
The surface‐water hydrographs of rivers exhibit large variations due to many natural phenomena. One of the most commonly used approaches for interpolating and extending streamflow records is to fit observed data with an analytic power model...
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DOI: https://doi.org/10.1061/(asce)0887-3801(1994)8:2(201)
Mohaiminul Islam, Guorong Chen, Shangzhu Jin • American Journal of Neural Networks and Applications
(2019)
Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in ...
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DOI: https://doi.org/10.11648/j.ajnna.20190501.12
Frank R. Burden, David Alan Winkler • Methods in molecular biology
(2008)
Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...
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DOI: https://doi.org/10.1007/978-1-60327-101-1_3