From distributed machine to distributed deep learning: a comprehensive survey
M Dehghani, Z Yazdanparast - Journal of Big Data, 2023 - Springer
Artificial intelligence has made remarkable progress in handling complex tasks, thanks to
advances in hardware acceleration and machine learning algorithms. However, to acquire …
advances in hardware acceleration and machine learning algorithms. However, to acquire …
Accumulated decoupled learning with gradient staleness mitigation for convolutional neural networks
Gradient staleness is a major side effect in decoupled learning when training convolutional
neural networks asynchronously. Existing methods that ignore this effect might result in …
neural networks asynchronously. Existing methods that ignore this effect might result in …
Toward model parallelism for deep neural network based on gradient-free ADMM framework
Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a
potential alternative optimizer to the Stochastic Gradient Descent (SGD) for deep learning …
potential alternative optimizer to the Stochastic Gradient Descent (SGD) for deep learning …
Cortico-cerebellar networks as decoupling neural interfaces
J Pemberton, E Boven, R Apps… - Advances in neural …, 2021 - proceedings.neurips.cc
The brain solves the credit assignment problem remarkably well. For credit to be assigned
across neural networks they must, in principle, wait for specific neural computations to finish …
across neural networks they must, in principle, wait for specific neural computations to finish …
A hybrid parallelization approach for distributed and scalable deep learning
SB Akintoye, L Han, X Zhang, H Chen, D Zhang - IEEE Access, 2022 - ieeexplore.ieee.org
Recently, Deep Neural Networks (DNNs) have recorded significant success in handling
medical and other complex classification tasks. However, as the sizes of DNN models and …
medical and other complex classification tasks. However, as the sizes of DNN models and …
A Survey From Distributed Machine Learning to Distributed Deep Learning
M Dehghani, Z Yazdanparast - arXiv preprint arXiv:2307.05232, 2023 - arxiv.org
Artificial intelligence has achieved significant success in handling complex tasks in recent
years. This success is due to advances in machine learning algorithms and hardware …
years. This success is due to advances in machine learning algorithms and hardware …
Energy-efficient DNN training processors on micro-AI systems
Many edge/mobile devices are now able to utilize deep neural networks (DNNs) thanks to
the development of mobile DNN accelerators. Mobile DNN accelerators overcame the …
the development of mobile DNN accelerators. Mobile DNN accelerators overcame the …
Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment
AG Ororbia - arXiv preprint arXiv:2312.09257, 2023 - arxiv.org
In this survey, we examine algorithms for conducting credit assignment in artificial neural
networks that are inspired or motivated by neurobiology, unifying these various processes …
networks that are inspired or motivated by neurobiology, unifying these various processes …
Approximate to be great: Communication efficient and privacy-preserving large-scale distributed deep learning in Internet of Things
The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A
deep learning technique is widely used to analyze the potential value of these data due to its …
deep learning technique is widely used to analyze the potential value of these data due to its …
Deep Reinforcement Learning With Multiple Unrelated Rewards for AGV Mapless Navigation
Mapless navigation for Automated Guided Vehicles (AGV) via Deep Reinforcement
Learning (DRL) algorithms has attracted significantly rising attention in recent years …
Learning (DRL) algorithms has attracted significantly rising attention in recent years …