21 papers · ranked by Valyu relevance
Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai + 6 more
'Xiaodan Liang' 'Zhiting Hu' 'Jinliang Wei' 'Pengtao Xie' 'Eric P. Xing'] Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization…
Matthias Langer, Zhen He, Wenny Rahayu, Yanbo Xue
—Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster. Developers of DDLS are required to make many decisions to process their particular workloads in their chosen environment efficiently. The advent of GPU-based deep learning, the ever-increasing…
Feng Liang, Zhen Zhang, Haifeng Lu, Victor C. M. Leung + 2 more
Comprehensive Survey Authors: ['Feng Liang' 'Zhen Zhang' 'Haifeng Lu' 'Victor C. M. Leung' 'Yanyi Guo' 'Xiping Hu'] Abstract—With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to…
Hassam Tahir, Eun-Sung Jung, Petros Daras
This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading…
Mohammad Dehghani, Zahra Yazdanparast
Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be…
Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg + 2 more
'Jeroen Kloppenburg' 'Tim Verbelen' 'Jan S. Rellermeyer'] The demand for arti!cial intelligence has grown signi!cantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, in order to increase the quality of…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Jilin Zhang, Hangdi Tu, Yongjian Ren, Jian Wan + 6 more
'Mingwei Li' 'Jue Wang' 'Lifeng Yu' 'Chang Zhao' 'Lei Zhang'] In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of…
Aswathy Ravikumar, Harini Sriraman, Davide Chicco
Background Pneumonia is a respiratory disease caused by bacteria; it affects many people, particularly in impoverished countries where pollution, unclean living standards, overpopulation, and insufficient medical infrastructures are prevalent. To guarantee curative therapy and boost survival chances, it is vital to…
Yasas Supeksala, Dinh C. Nguyen, Ming Ding, Thilina Ranbaduge + 4 more
'Calson Chua' 'Jun Zhang' 'Jun Li' 'H. Vincent Poor'] YASAS SUPEKSALA, Swinburne University of Technology, Australia DINH C. NGUYEN, Purdue University, USA MING DING, DATA61-CSIRO, Australia THILINA RANBADUGE, DATA61-CSIRO, Australia CALSON CHUA, Swinburne University of Technology, Australia JUN ZHANG, Swinburne…
Haijie Pan, Lirong Zheng, Sylvain Girard
Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based on variance reduction, named DisSAGD, is proposed in this study.…
John X. Qiu, Hong-Jun Yoon, Kshitij Srivastava, Thomas P. Watson + 5 more
Background Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of…
Bishal Thapaliya, Riyasat Ohib, Eloy Geenjar, Jingyu Liu + 2 more
Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated…
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
It is essential to be able to combine datasets across imaging centres to represent the breadth of biological variability present in clinical populations. This, however, leads to two challenges: first, an increase in non-biological variance due to scanner differences, known as the harmonisation problem, and, second…
Ivan Rodriguez-Conde, Celso Campos, Florentino Fdez-Riverola, Antonio Fernández-Caballero + 1 more
'Antonio Fernández-Caballero' 'Juan M. Corchado'] Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the…
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…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
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…
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…
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
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…