[HTML][HTML] A multi-objective approach for communication reduction in federated learning under devices heterogeneity constraints
Federated learning is a paradigm that proposes protecting data privacy by sharing local
models instead of raw data during each iteration of model training. However, these models …
models instead of raw data during each iteration of model training. However, these models …
Wrht: Efficient all-reduce for distributed DNN training in optical interconnect systems
Communication efficiency is crucial for accelerating distributed deep neural network (DNN)
training. All-reduce, a vital communication primitive, is responsible for reducing model …
training. All-reduce, a vital communication primitive, is responsible for reducing model …
xCCL: A Survey of Industry-Led Collective Communication Libraries for Deep Learning
Abstract Machine learning techniques have become ubiquitous both in industry and
academic applications. Increasing model sizes and training data volumes necessitate fast …
academic applications. Increasing model sizes and training data volumes necessitate fast …
Deep representation learning: Fundamentals, perspectives, applications, and open challenges
KT Baghaei, A Payandeh, P Fayyazsanavi… - arXiv preprint arXiv …, 2022 - arxiv.org
Machine Learning algorithms have had a profound impact on the field of computer science
over the past few decades. These algorithms performance is greatly influenced by the …
over the past few decades. These algorithms performance is greatly influenced by the …
Where is my training bottleneck? hidden trade-offs in deep learning preprocessing pipelines
Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep
the training processes busy. Maximizing resource utilization is becoming more challenging …
the training processes busy. Maximizing resource utilization is becoming more challenging …
Deep learning augmented data assimilation: Reconstructing missing information with convolutional autoencoders
Remote sensing data play a critical role in improving numerical weather prediction (NWP).
However, the physical principles of radiation dictate that data voids frequently exist in …
However, the physical principles of radiation dictate that data voids frequently exist in …
A comprehensive survey on deep neural image deblurring
SA Biyouki, H Hwangbo - arXiv preprint arXiv:2310.04719, 2023 - arxiv.org
Image deblurring tries to eliminate degradation elements of an image causing blurriness
and improve the quality of an image for better texture and object visualization. Traditionally …
and improve the quality of an image for better texture and object visualization. Traditionally …
Taming resource heterogeneity in distributed ml training with dynamic batching
Current techniques and systems for distributed model training mostly assume that clusters
are comprised of homogeneous servers with a constant resource availability. However …
are comprised of homogeneous servers with a constant resource availability. However …
[HTML][HTML] Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture
Artificial intelligence (AI) research and market have grown rapidly in the last few years, and
this trend is expected to continue with many potential advancements and innovations in this …
this trend is expected to continue with many potential advancements and innovations in this …
AI governance for businesses
J Schneider, R Abraham, C Meske, J Brocke - arXiv preprint arXiv …, 2020 - arxiv.org
Artificial Intelligence (AI) governance regulates the exercise of authority and control over the
management of AI. It aims at leveraging AI through effective use of data and minimization of …
management of AI. It aims at leveraging AI through effective use of data and minimization of …