Results 61 to 70 of about 9,534,538 (365)
Generalizable and efficient cross‐domain person re‐identification model using deep metric learning
IET Computer Vision, 2023 Most of the successful person re‐ID models conduct supervised training and need a large number of training data. These models fail to generalise well on unseen unlabelled testing sets.
Saba Sadat Faghih Imani +2 more
doaj +1 more source
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
core +3 more sources
SN Computer Science, 2021 Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0).
Iqbal H. Sarker
semanticscholar +1 more source
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
core +1 more source
Deep learning in the fog [PDF]
International Journal of Distributed Sensor Networks, 2019 In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user.
Andrzej Sobecki +3 more
openaire +4 more sources
MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework [PDF]
, 2019 As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources.
Kang, Byungkon +4 more
core +1 more source
SIAM Journal on Mathematics of Data Science, 2021 Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined.
Laurent El Ghaoui +4 more
openaire +2 more sources
Deep Neural Networks for Form-Finding of Tensegrity Structures
Mathematics, 2022 Analytical paradigms have limited conventional form-finding methods of tensegrities; therefore, an innovative approach is urgently needed. This paper proposes a new form-finding method based on state-of-the-art deep learning techniques.
Seunghye Lee +3 more
doaj +1 more source
Deep learning approach to scalable imaging through scattering media [PDF]
, 2019 We propose a deep learning technique to exploit “deep speckle correlations”. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.Published ...
Li, Yunzhe, Tian, Lei, Xue, Yujia
core +1 more source
Deep learning with convolutional neural networks for EEG decoding and visualization [PDF]
Human Brain Mapping, 2017 Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data.
R. Schirrmeister +8 more
semanticscholar +1 more source