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Auto-encoders in deep learning—a review with new perspectives
S Chen, W Guo - Mathematics, 2023 - mdpi.com
Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …
development of neural networks. The auto-encoder is a key component of deep structure …
Deep learning sensor fusion for autonomous vehicle perception and localization: A review
Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …
Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data
Deep learning, a state-of-the-art machine learning approach, has shown outstanding
performance over traditional machine learning in identifying intricate structures in complex …
performance over traditional machine learning in identifying intricate structures in complex …
A decade survey of content based image retrieval using deep learning
The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …
against a query image. Generally, the similarity between the representative features of the …
[کتاب][B] Deep learning
I Goodfellow, Y Bengio, A Courville, Y Bengio - 2016 - synapse.koreamed.org
Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their
careers in deep learning and artificial intelligence research. The other target audience …
careers in deep learning and artificial intelligence research. The other target audience …
Full resolution image compression with recurrent neural networks
This paper presents a set of full-resolution lossy image compression methods based on
neural networks. Each of the architectures we describe can provide variable compression …
neural networks. Each of the architectures we describe can provide variable compression …
Joint unsupervised learning of deep representations and image clusters
In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …
representations and image clusters. In our framework, successive operations in a clustering …
Soft-to-hard vector quantization for end-to-end learning compressible representations
We present a new approach to learn compressible representations in deep architectures
with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation …
with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation …
[HTML][HTML] Pathology image analysis using segmentation deep learning algorithms
With the rapid development of image scanning techniques and visualization software, whole
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …
Deep learning code fragments for code clone detection
Code clone detection is an important problem for software maintenance and evolution. Many
approaches consider either structure or identifiers, but none of the existing detection …
approaches consider either structure or identifiers, but none of the existing detection …