29 papers · ranked by Valyu relevance
Benjamin D. Lee, Anthony Gitter, Casey S. Greene, Sebastian Raschka + 17 more
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling, as opposed to having domain experts developing rules for prediction…
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…
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…
Taehoon Kim
| 1 | | Introduction to Key Concepts in Machine Learning and Deep Learning | 12 | | --- | --- | --- | --- | | | 1.1 | Introduction to Machine Learning | 12 | | | 1.2 | Supervised Learning Fundamentals | 13 | | | 1.2.1 | Generalization to New Examples | 14 | | | 1.2.2 | Underfitting and Overfitting | 14 | | | 1.3 |…
Jiasheng Wang
Deep learning (DL), a subfield of machine learning, has made remarkable strides across various aspects of medicine. This review examines DL’s applications in hematology, spanning from molecular insights to patient care. The review begins by providing a straightforward introduction to the basics of DL tailored for those…
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…
HOSSEIN MORADIMOKHLES, GWO-JEN HWANG, HOSSEIN ZANGENEH, MARYAM POURJAMSHIDI + 1 more
The term “deep learning” has incorrect interpretations in education and technology disciplines. It describes a method of learning in which the objective is to achieve an in-depth understanding of topic rather than succumb to surface learning. "Non-surface learning" is undeniably related to deep information…
Tehreem Qamar, Narmeen Zakaria Bawany, Rong Qu
Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for…
Yanru Jiang, Rick Dale
There is an important challenge in systematically interpreting the internal representations of deep neural networks. This study introduces a multi-dimensional quantification and visualization approach which can capture two temporal dimensions of a model learning experience: the “information processing trajectory” and…
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…
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)…
Federico Fontana
This chapter is organized as follows. In section 2.1 there is an overview of machine learning tasks and learning paradigms. In section 2.2 the common approaches and networks are theoretically explored. In section 2.3 approaches and network more efficient in speed and memory are presented. Machine learning is…
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…
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…
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…
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…
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…
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…
Authors not listed
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery…
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…
Maria K. Eckstein, Christopher Summerfield, Nathaniel D. Daw, Kevin J. Miller
Quantitative models of behavior are a fundamental tool in cognitive science. Typically, models are hand-crafted to implement specific cognitive mechanisms. Such “classic” models are interpretable by design, but may provide poor fit to experimental data. Artificial neural networks (ANNs), on the contrary, can fit…
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…
Ittai Shamir, Yaniv Assaf
Advancements in neuroscience and artificial intelligence have been fueling one another for decades. In this study, we integrate a neuroimaging model of laminar-level connectomics into a biologically-inspired deep learning model of recurrent neural networks (RNNs) for working memory tasks. The resulting model offers a…
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…
Pau Vilimelis Aceituno, Matilde Tristany Farinha, Reinhard Loidl, Benjamin F. Grewe
A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. These same…
Shue Shiinoki, Seitaro Iwama, Junichi Ushiba
Brain-computer interface (BCI) control enables direct communication between the brain and external devices. However, BCI control accuracy with intention inferred from non-invasive modalities is limited, even when using data-driven approaches to tailor neural decoders. In this study, we propose a knowledge-driven…
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…