27 papers · ranked by Valyu relevance
Lina Ma, Tao Yang
In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, with its uneven Healthcare imaging resources, high Healthcare imaging costs, and diagnoses often relying on doctors' clinical experience and equipment level limitations…
Malik YOUSEF, Jens ALLMER
Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic…
Yanming Zhu, Min Wang, Xuefei Yin, Jue Zhang + 3 more
'Jiankun Hu' 'Yang Yue'] Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have…
Juan de Dios Rojas Olvera, Isidro Gómez-Vargas, J. Alberto Vázquez
In cosmology, the analysis of observational evidence is very important to test theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and are recently being considered in the analysis of cosmological data. The main goal of this paper is to…
M. Akif Yenikaya, Gökhan Kerse, Onur Oktaysoy
2.1### Deep learning and its types Artificial intelligence is a machine or computer application that imitates human-specific features such as reasoning, learning or communicating, and therefore exhibits human-like behaviors (29). This practice is also used to characterize the Fourth Industrial Revolution (30) and…
Fazle Rabbi, Sajjad Rahmani Dabbagh, Pelin Angin, Ali Kemal Yetisen + 2 more
'Savas Tasoglu' 'Shohei Yamamura'] Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular…
Yansel Gonzalez Tejeda, Helmut A. Mayer
Supervised Regression (Preprint) Authors: ['Yansel Gonzalez Tejeda' 'Helmut A. Mayer'] In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we…
Eliška Chalupová, Ondřej Vaculík, Jakub Poláček, Filip Jozefov + 2 more
The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)…
Xiaoyan Jiang, Zuojin Hu, Shuihua Wang, Yudong Zhang + 1 more
Simple Summary Deep learning has succeeded greatly in medical image-based cancer diagnosis. To help readers better understand the current research status and ideas, this article provides a detailed overview of the working mechanisms and use cases of commonly used radiological imaging and histopathology, the basic…
Matteo Zambra, Alberto Testolin, Marco Zorzi
Background: Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in triggering the deep learning revolution, demonstrating for the very first time the feasibility of unsupervised learning in…
Abhishek Gupta, Sruthi S. Nair, Raunak Joshi, Vidya Chitre
Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering changes in very deep models with many regularization based techniques. In this paper we train a deep neural…
HOSSEIN MORADIMOKHLES, GWO-JEN HWANG, HOSSEIN ZANGENEH, MARYAM POURJAMSHIDI + 1 more
Three waves make up the evolution of deep learning. In 1940s-1960s, cybernetics with biological learning theories emerged as the first wave of DL. The following phase is connectionism, which used back-propagation to develop a human brain with various complexities in 1980s and early 1990s. The current circumstance under…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Abhishek Gupta, Raunak Joshi, Ronald Melwin Laban
Image Forgery is a problem of image forensics and its detection can be leveraged using Deep Learning. In this paper we present an approach for identification of authentic and tampered images done using image editing tools with Error Level Analysis and Convolutional Neural Network. The process is performed on CASIA ITDE…
Geordie Williamson
Over the last decade, deep learning has found countless applications throughout industry and science. However, its impact on pure mathematics has been modest. This is perhaps surprising, as some of the tasks at which deep learning excels—like playing the board-game Go or finding patterns in complicated…
Danny da Costa, Rainer Goebel, Mario Senden
The distribution of retinal ganglion cells in primate visual systems portrays a densely distributed central region, with an incrementally decreasing cell density as the angle of visual eccentricity increases. This results in a non-uniform sampling of the retinal image that resembles a wheelbarrow distortion. We propose…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
Saeed Iqbal, Adnan N. Qureshi, Jianqiang Li, Tariq Mahmood
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN…
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen + 2 more
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously…
Andrew McNutt, Yanjing Li, Paul Francoeur, David Koes
Knowledge of the bound protein-ligand structure is critical to many drug discovery tasks. One tool for in silico bound structure elucidation is molecular docking, which samples and scores ligand binding conformations. Recent work has demonstrated that convolutional neural networks (CNNs) for protein-ligand pose scoring…
Rishabh Raj, C. Ron Yu
Our recognition of an object is consistent across conditions, unaffected by motion, perspective, rotation, and corruption. This robustness is thought to be enabled by invariant object representations, but how the brain achieves it remains unknown^1^. In artificial neural networks, learning to represent objects is…
Camilo J. Mininni, B. Silvano Zanutto
A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypothesis regarding the algorithms instantiated in the dynamic of a neural population, its firing statistics, and the underlying…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…