26 papers · ranked by Valyu relevance
Chaopeng Shen
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data…
Gary Marcus
What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be…
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…
Nicholas G. Polson, Vadim Sokolov
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques…
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…
Nicholas G. Polson, Vadim Sokolov
Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state-ofthe-art of deep learning from a modeling and algorithmic…
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…
Authors not listed
The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…
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…
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…
Sanjay Chawla, Preslav Nakov, Ahmed Ali, Wendy Hall + 6 more
'Xiaosong Ma' 'Hüsrev Taha Sencar' 'Ingmar Weber' 'Michael Wooldridge' 'Ting Yu'] It is ten years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on Artificial Intelligence (AI). Supervised Learning for cognitive tasks is effectively solved — provided…
Angela Lopez-del Rio, Alfons Nonell-Canals, David Vidal, Alexandre Perera-Lluna
Binding prediction between targets and drug-like compounds through Deep Neural Networks have generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classification models is still an issue to be addressed. In this work, we…
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…
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…
David Buterez, Jon Paul Janet, Steven J. Kiddle, Dino Oglic + 1 more
Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to…
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…