Results 41 to 50 of about 10,369,018 (353)
The Limitations of Deep Learning in Adversarial Settings [PDF]
European Symposium on Security and Privacy, 2015 Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks.
Nicolas Papernot +5 more
semanticscholar +1 more source
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
A survey on Image Data Augmentation for Deep Learning
Journal of Big Data, 2019 Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very
Connor Shorten, T. Khoshgoftaar
semanticscholar +1 more source
Convolutional neural networks for heat conduction
Case Studies in Thermal Engineering, 2022 This paper presents a data-driven approach to solve heat conduction problems, in particular 2D heat conduction problems. The physical laws which govern such problems are modeled by partial differential equations.
Sidharth Tadeparti +1 more
doaj +1 more source
Robust deep learning based protein sequence design using ProteinMPNN
bioRxiv, 2022 While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta.
J. Dauparas +21 more
semanticscholar +1 more source
Applied Sciences, 2021 To properly restore masonry cultural heritage sites, the materials used for retrofitting can have a critical effect, and this requires standards for traditional Korean brick and lime mortar to be examined.
Gayoon Lee +4 more
doaj +1 more source
Deep learning in remote sensing: a review [PDF]
, 2017 Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich +6 more
core +4 more sources
Deep learning in bioinformatics [PDF]
Briefings in Bioinformatics, 2016 In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields.
Min, Seonwoo +2 more
openaire +3 more sources
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 for time series classification: a review [PDF]
Data mining and knowledge discovery, 2018 Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed.
Hassan Ismail Fawaz +4 more
semanticscholar +1 more source