Deep learning for cellular image analysis

E Moen, D Bannon, T Kudo, W Graf, M Covert… - Nature …, 2019 - nature.com
Recent advances in computer vision and machine learning underpin a collection of
algorithms with an impressive ability to decipher the content of images. These deep learning …

The future of memristors: Materials engineering and neural networks

K Sun, J Chen, X Yan - Advanced Functional Materials, 2021 - Wiley Online Library
Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …

Artificial intelligence: a survey on evolution, models, applications and future trends

Y Lu - Journal of Management Analytics, 2019 - Taylor & Francis
Artificial intelligence (AI) is one of the core drivers of industrial development and a critical
factor in promoting the integration of emerging technologies, such as graphic processing …

Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning

N Papernot, P McDaniel - arxiv preprint arxiv:1803.04765, 2018 - arxiv.org
Deep neural networks (DNNs) enable innovative applications of machine learning like
image recognition, machine translation, or malware detection. However, deep learning is …

Spike sorting for large, dense electrode arrays

C Rossant, SN Kadir, DFM Goodman, J Schulman… - Nature …, 2016 - nature.com
Developments in microfabrication technology have enabled the production of neural
electrode arrays with hundreds of closely spaced recording sites, and electrodes with …

Two hundred years of zooplankton vertical migration research

K Bandara, Ø Varpe, L Wijewardene… - Biological …, 2021 - Wiley Online Library
Vertical migration is a geographically and taxonomically widespread behaviour among
zooplankton that spans across diel and seasonal timescales. The shorter‐term diel vertical …

A survey on platforms for big data analytics

D Singh, CK Reddy - Journal of big data, 2015 - Springer
The primary purpose of this paper is to provide an in-depth analysis of different platforms
available for performing big data analytics. This paper surveys different hardware platforms …

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

A Koutsoukas, KJ Monaghan, X Li, J Huan - Journal of cheminformatics, 2017 - Springer
Background In recent years, research in artificial neural networks has resurged, now under
the deep-learning umbrella, and grown extremely popular. Recently reported success of DL …

[HTML][HTML] Modern computing: Vision and challenges

SS Gill, H Wu, P Patros, C Ottaviani, P Arora… - … and Informatics Reports, 2024 - Elsevier
Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

Energy aware edge computing: A survey

C Jiang, T Fan, H Gao, W Shi, L Liu, C Cérin… - Computer …, 2020 - Elsevier
Edge computing is an emerging paradigm for the increasing computing and networking
demands from end devices to smart things. Edge computing allows the computation to be …