Results 11 to 20 of about 2,611,781 (277)
International Journal Vallis Aurea, 2021 In the last decade, there has been a significant increase in the number of papers related to machine learning and the application of machine learning in various fields of science. Belmonte et al. observed that between 2010 and 2018, the growth in the number of papers related to machine learning topics and big data was exponential.
Đokić, Kristian +2 more
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IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons.
Enrique S. Marquez +2 more
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Building Program Vector Representations for Deep Learning [PDF]
, 2014 Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc.
Jin, Zhi +6 more
core +1 more source
Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras [PDF]
, 2018 Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image,
Benito Picazo, Jesús +4 more
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Deep Learning in Medicine [PDF]
International Journal of Trend in Scientific Research and Development, 2019 Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks ...
Tarun Jaiswal, Sushma Jaiswal
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Deep Learning in Proteomics [PDF]
PROTEOMICS, 2020 AbstractProteomics, the study of all the proteins in biological systems, is becoming a data‐rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post‐translational modifications (PTMs) can be studied in a variety of
Bo Wen +6 more
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Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction [PDF]
, 2017 Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision
Kumar, Devinder +2 more
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Approximations in Deep Learning
, 2022 Approximate Computing Techniques - From Component- to Application-Level, pp.467-512, 2022, 978-3-030-94704 ...
Dupuis, Etienne +5 more
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A deep-learning approach for high-speed Fourier ptychographic microscopy [PDF]
, 2018 We demonstrate a new convolutional neural network architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM.https://www.researchgate.net ...
Li, Yunzhe +5 more
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Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source