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DELLY: structural variant discovery by integrated paired-end and split-read analysis
Bioinformatics, 2012 Motivation: The discovery of genomic structural variants (SVs) at high sensitivity and specificity is an essential requirement for characterizing naturally occurring variation and for understanding pathological somatic rearrangements in personal genome ...
Tobias Rausch +2 more
exaly +2 more sources
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Science, 2018 Alexander B Rosenberg +2 more
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Efficient Parallel Split Learning Over Resource-Constrained Wireless Edge Networks [PDF]
IEEE Transactions on Mobile Computing, 2023 The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge
Zhengyi Lin +6 more
semanticscholar +1 more source
ResNeSt: Split-Attention Networks [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise ...
Hang Zhang +11 more
semanticscholar +1 more source
Split Learning in 6G Edge Networks [PDF]
IEEE wireless communications, 2023 With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorporate federated learning into the mobile edge has gained considerable ...
Zhengyi Lin +3 more
semanticscholar +1 more source
Music Source Separation With Band-Split RNN [PDF]
IEEE/ACM Transactions on Audio Speech and Language Processing, 2022 The performance of music source separation (MSS) models has been greatly improved in recent years thanks to the development of novel neural network architectures and training pipelines. However, recent model designs for MSS were mainly motivated by other
Yi Luo, Jianwei Yu
semanticscholar +1 more source
Split Learning Over Wireless Networks: Parallel Design and Resource Management [PDF]
IEEE Journal on Selected Areas in Communications, 2022 Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer.
Wen Wu +7 more
semanticscholar +1 more source
SplitFed: When Federated Learning Meets Split Learning [PDF]
AAAI Conference on Artificial Intelligence, 2020 Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data.
Chandra Thapa +2 more
semanticscholar +1 more source
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges [PDF]
ACM Computing Surveys, 2021 Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.
Yoshitomo Matsubara +2 more
semanticscholar +1 more source
Splitting split supersymmetry [PDF]
Physical Review D, 2005 In split supersymmetry, the supersymmetric scalar particles are all very heavy, at least at the order of 10{sup 9} GeV, but the gauginos, Higgsinos, and one of the neutral Higgs bosons remain below a TeV. Here we further split the split supersymmetry by taking the Higgsino mass parameter {mu} to be very large.
Kingman Cheung, Cheng-Wei Chiang
openaire +3 more sources