13 papers · ranked by Valyu relevance
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili + 6 more
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or…
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin + 32 more
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often…
Frank Emmert-Streib, Zhen Yang, Han Feng, Shailesh Tripathi + 1 more
'Matthias Dehmer'] Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem…
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin + 23 more
Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We…
Binhua Tang, Zixiang Pan, Kang Yin, Asif Khateeb
Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference…
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…
Osval Antonio Montesinos‐López, Abelardo Montesinos‐López, Carlos Moises Hernandez‐Suarez, José Alberto Barrón‐López + 1 more
Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but nowadays, artificial…
Md. Shoaibur Rahman
This article presents an overview of the generalized formulations of the computations, optimization, and tuning of a deep feedforward neural network. A small network has been used to systematically explain the computing steps, which were then used to establish the generalized forms of the computations in forward and…
Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li + 2 more
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities…
Ping Gong, Chaoyang Zhang, Minjun Chen
Deep learning (DL), alsocalled deep structured learning or hierarchical learning, is an important subset of machine learning (ML). The distinction between DL and conventional “shallow” ML is that DL algorithms allow computational models composed of multiple processing layers to be fed with raw data and automatically…
Xiayu Chen, Ming Zhou, Zhengxin Gong, Wei Xu + 4 more
Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks through an end-to-end deep learning strategy. Deep learning gives rise to data representations with multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs.…
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…
Najib J. Majaj, Denis G. Pelli
Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This…