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A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
Communication-efficient edge AI: Algorithms and systems
Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …
ranging from speech processing, image classification to drug discovery. This is driven by the …
Gan inversion: A survey
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …
model so that the image can be faithfully reconstructed from the inverted code by the …
Survey of vector database management systems
There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …
produced within the past five years. But embedding-based retrieval has been studied for …
[كتاب][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …
McDonald Neural networks were developed to simulate the human nervous system for …
[HTML][HTML] Machine learning in acoustics: Theory and applications
Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …
communications to ocean and Earth science. We survey the recent advances and …
A high-bias, low-variance introduction to machine learning for physicists
Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …
research and application. The purpose of this review is to provide an introduction to the core …
Dynamical variational autoencoders: A comprehensive review
Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …
represent high-dimensional complex data through a low-dimensional latent space learned …
An introduction to neural data compression
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …
methods to data compression. Recent advances in statistical machine learning have opened …
Compressing volumetric radiance fields to 1 mb
Approximating radiance fields with discretized volumetric grids is one of promising directions
for improving NeRFs, represented by methods like DVGO, Plenoxels and TensoRF, which …
for improving NeRFs, represented by methods like DVGO, Plenoxels and TensoRF, which …