30 papers · ranked by Valyu relevance
Asmaul Hosna, Ethel Merry, Jigmey Gyalmo, Zulfikar Alom + 2 more
'Mohammad Abdul Azim'] Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML…
Gelan Ayana, Kokeb Dese, Se-woon Choe, Ognjen Arandjelović
Simple Summary Transfer learning plays a major role in medical image analyses; however, obtaining adequate training image datasets for machine learning algorithms can be challenging. Although many studies have attempted to employ transfer learning in medical image analyses, thus far, only a few review articles…
Chanhoe Gu, Minhyeok Lee, Andrea Cataldo, Ming Liu + 2 more
'Qingliang Jiao'] Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel…
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…
Muhammad Safdar Ali Khan, Arif Husen, Shafaq Nisar, Hasnain Ahmed + 3 more
'Syed Shah Muhammad' 'Shabib Aftab' 'Hongfei Hou'] Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models…
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi + 4 more
'Hengshu Zhu' 'Hui Xiong' 'Qing He'] Abstract—Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledg e contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing…
Wancheng Huang, Yuan Tian, Sathishkumar Veerappampalayam Easwaramoorthy
'Sathishkumar Veerappampalayam Easwaramoorthy'] Reservoir reconstruction, where parameter prediction plays a key role, constitutes an extremely important part in oil and gas reservoir exploration. With the mature development of artificial intelligence, parameter prediction methods are gradually shifting from previous…
Mohamad Zamini, Eun‐jin Kim
—The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data. Transfer learning approaches compared to traditional machine learning approaches are capable of modeling better data patterns from the current domain. However, vanilla TL needs performance…
Lina Chato, Emma Regentova, Pascale Gauthier
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to…
A.K. Panda, Damodar Panigrahi, Shaswata Mitra, Sudip Mittal + 1 more
Progress, Limitations, and Opportunities Authors: ['A.K. Panda' 'Damodar Panigrahi' 'Shaswata Mitra' 'Sudip Mittal' 'Shahram Rahimi'] The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rulebased algorithms, resulting in limited accuracy. The introduction of machine…
Frederico Guth, Teofilo E. deCampos
Humans can learn from very few samples, demonstrating an outstanding generalization ability that learning algorithms are still far from reaching. Currently, the most successful models demand enormous amounts of well-labeled data, which are expensive and difficult to obtain, becoming one of the biggest obstacles to the…
Sandhya Aneja, Nagender Aneja, Pg Emeroylariffion Abas, Abdul Ghani Naim
'Abdul Ghani Naim'] Article Info ABSTRACT Article history: Received Jul 19, 2021 Revised Nov 30, 2021 Accepted Dec 12, 2021 Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task. In modern computer vision research, the question is which architecture…
Abolfazl Farahani, Behrouz Pourshojae, Khaled Rasheed, Hamid R. Arabnia
'Hamid R. Arabnia'] Abstract—The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a…
Hossein Sharifi-Noghabi, Shuman Peng, Olga Zolotareva, Colin C. Collins + 1 more
The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets…
Ildefons Magrans de Abril, Marina Garrett, Douglas R. Ollerenshaw, Peter A. Groblewski + 2 more
Animals are known to be able to rapidly transfer knowledge between tasks with similar structure. We trained a set of mice on a visual change detection task with multiple stages, starting with direct transitions between gratings, adding an intervening gray screen, and subsequently moving to multiple sets of natural…
Gunther Eysenbach, Lingfei Mo, Gregory Norman, Jose Juan Dominguez Veiga + 4 more
Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines…
Dominik Garber, József Fiser
Transfer learning, the re-application of previously learned higher-level regularities to novel input, is a key challenge in cognition. While previous empirical studies investigated human transfer learning in supervised or reinforcement learning for explicit knowledge, it is unknown whether such transfer occurs during…
Yan Gao, Yan Cui
Large-scale clinical and biomedical datasets increasingly contain both diverse subgroup attributes (e.g., demographic or clinical subgroups) and multiple prediction targets. Although various machine learning approaches can address subgroup differences or multi-target prediction, they often consider these aspects…
Mohammadreza Iman, Khaled Rasheed, Hamid R. Arabnia
Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two major constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such dependency and costs…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
S. Maryam Hosseini, Abubakr Shafique, Morteza Babaie, H.R. Tizhoosh
In dealing with the lack of sufficient annotated data and in contrast to supervised learning, unsupervised, self-supervised, and semi-supervised domain adaptation methods are promising approaches, enabling us to transfer knowledge from rich labeled source domains to different (but related) unlabeled target domains…
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…
Patrick S. Stumpf, Doris Du, Haruka Imanishi, Yuya Kunisaki + 10 more
Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research…
Authors not listed
The performance of electrochemical cells for energy storage and conversion, such as batteries and fuel cells, can be improved by optimizing their manufacturing processes. This can be very time consuming and costly through the conventional trial-and-error approaches. Machine Learning (ML) models can help to accelerate…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Adam Eichenbaum, Jason M. Scimeca, Mark D’Esposito
Humans can draw insight from previous experiences in order to quickly adapt to novel environments that share a common underlying structure. Here we combine functional imaging and computational modeling to identify the neural systems that support the discovery and transfer of hierarchical task structure. Human subjects…
Jo Plested, Tom Gedeon
Deep neural networks such as convolutional neural networks (CNNs) and transformers have achieved many successes in image classification in recent years. It has been consistently demonstrated that best practice for image classification is when large deep models can be trained on abundant labelled data. However there are…
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
Authors not listed
The accurate prediction of fuel mixture properties is essential for the development of alternative fuels, yet remains challenging under data-scarce conditions due to the combinatorial complexity of multi-component systems. In this study, we present a systematic evaluation of three machine learning (ML)…
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…