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Academic Articles & Research Papers
Showing 20 of 32 academic articles
Raviraju Balappa D, Gautam Kumar Rajput • Shodh Sari
(2025)
Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, ma...
DOAJ
Open Access (DOAJ)
DOI: https://doi.org/10.59231/SARI7840
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
OPENALEX
cc-by-nc-nd
DOI: https://doi.org/10.1162/coli_r_00312
Santanu Pattanayak • Apress eBooks
(2022)
In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...
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DOI: https://doi.org/10.1007/978-1-4842-8931-0_4
Martin Sundermeyer, Ralf Schlüter, Hermann Ney • Interspeech (USB)
(2012)
Neural networks have become increasingly popular for the task of language modeling.Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks ca...
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Open Access
DOI: https://doi.org/10.21437/interspeech.2012-65
Danilo P. Mandic, Jonathon A. Chambers • Wiley eBooks
(2001)
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...
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DOI: https://doi.org/10.1002/047084535x
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112
Richard J. Mammone (1994)
Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peri...
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Mike Schuster, Kuldip K. Paliwal • IEEE Transactions on Signal Processing
(1997)
In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future f...
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DOI: https://doi.org/10.1109/78.650093
C. H. Chen • McGraw-Hill, Inc. eBooks
(1996)
Fuzzy logic principles and algorithms design guidelines for fuzzy logic systems enhanced training algorithms maximum likelihood training temporal difference learning NADINE hybrid learning systems explanation and reasoning within connectivi...
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Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University)
(2017)
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.02515
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals • arXiv (Cornell University)
(2014)
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1409.2329
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1609.07061
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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• CRC Press eBooks
(1999)
From the Publisher:
With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design ...
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DOI: https://doi.org/10.1201/9781420049176
Olusola Bamisile, Dongsheng Cai, Humphrey Adun, Chukwuebuka Ejiyi et al. • Energy Reports
(2023)
The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic ...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.egyr.2023.05.229
Zhewei Zhang, Huzi Cheng, Tianming Yang • PLoS Computational Biology
(2020)
The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. Th...
DOAJ
Open Access (DOAJ)
DOI: 10.1371/journal.pcbi.1008342
Palash Goyal, Sumit Kumar Pandey, Karan Jain • Apress eBooks
(2018)
Discover the concepts of deep learning used for natural language processing (NLP) in this practical book, with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2...
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DOI: https://doi.org/10.1007/978-1-4842-3685-7
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
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cc-by
DOI: https://doi.org/10.1093/nsr/nwx110
Yumin Zhang, Yanyong Wang • EURASIP Journal on Image and Video Processing
(2025)
Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s13640-025-00677-0
Muhammad Salihin Saealal, Mohd Zamri Ibrahim, David. J. Mulvaney, Mohd Ibrahim Shapiai et al. • PLoS ONE
(2022)
Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes ar...
DOAJ
Open Access (DOAJ)