Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …
learning models. However, their estimation is complex from both theoretical and …
Machine-learned potentials for next-generation matter simulations
The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …
fundamental trade-off: bridging large time-and length-scales with highly accurate …
Compute trends across three eras of machine learning
Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …
Structural crack detection using deep convolutional neural networks
Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …
range of computer vision problems. It has achieved encouraging results in numerous …
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …
impact on every industry and research discipline. At the core of this revolution lies the tools …
[ΒΙΒΛΙΟ][B] Deep learning
JD Kelleher - 2019 - books.google.com
An accessible introduction to the artificial intelligence technology that enables computer
vision, speech recognition, machine translation, and driverless cars. Deep learning is an …
vision, speech recognition, machine translation, and driverless cars. Deep learning is an …
Deep convolutional neural networks for image classification: A comprehensive review
Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …
1980s. However, despite a few scattered applications, they were dormant until the mid …
Deep learning‐based crack damage detection using convolutional neural networks
A number of image processing techniques (IPTs) have been implemented for detecting civil
infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs …
infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs …
SDDNet: Real-time crack segmentation
This article reports the development of a pure deep learning method for segmenting
concrete cracks in images. The objectives are to achieve the real-time performance while …
concrete cracks in images. The objectives are to achieve the real-time performance while …
[ΒΙΒΛΙΟ][B] Deep learning
I Goodfellow - 2016 - books.google.com
An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …
conceptual background, deep learning techniques used in industry, and research …