AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices | Paper Citation Record

Observation 04bc977c-8e79-4885-9a8b-011142fb6b71 · outbound

This paper cites Autonomous driving assistance with dynamic objects using traffic surveillance cameras,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Autonomous driving assistance with dynamic objects using traffic surveillance cameras,

Reference 1

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Observation 816160be-c6c1-4512-a3c3-69151fe132c3 · outbound

This paper cites Roadside infrastructure support for urban automated driving,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Roadside infrastructure support for urban automated driving,

Reference 2

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Observation e4467cfb-9487-498c-95a7-8f61e9168af7 · outbound

This paper cites Enabling resource-efficient aiot system with cross-level optimization: A survey,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Enabling resource-efficient aiot system with cross-level optimization: A survey,

Reference 3

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Observation 1c4bb043-cbf7-4a0b-89ce-3f15ba30d204 · outbound

This paper cites Surgical and medical applications of drones: A comprehensive review,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Surgical and medical applications of drones: A comprehensive review,

Reference 4

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This paper cites A survey on behav- ioral biometric authentication on smartphones,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices A survey on behav- ioral biometric authentication on smartphones,

Reference 5

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Observation 75554145-9361-49a5-8158-addb778cf266 · outbound

This paper cites Moodexplorer: Towards com- pound emotion detection via smartphone sensing,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Moodexplorer: Towards com- pound emotion detection via smartphone sensing,

Reference 6

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Observation debd9c5b-8deb-4b98-ace9-2ac531a8cecf · outbound

This paper cites Attrleaks on the edge: Exploiting information leakage from privacy-preserving co- inference,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Attrleaks on the edge: Exploiting information leakage from privacy-preserving co- inference,

Reference 7

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Observation ad397744-e4e1-43ce-a2a5-a68d08e1cee2 · outbound

This paper cites nnperf: Demystifying dnn runtime inference latency on mobile platforms,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices nnperf: Demystifying dnn runtime inference latency on mobile platforms,

Reference 8

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Observation 50092d7f-7187-4357-b10c-a5056dd7faf1 · outbound

This paper cites Deep learning on mobile and embedded devices: State-of-the-art, challenges, and future directions,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Deep learning on mobile and embedded devices: State-of-the-art, challenges, and future directions,

Reference 9

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Observation d3e85fbf-3c96-4b93-b2b3-8f1cfe3ff3c9 · outbound

This paper cites Adaspring: Context- adaptive and runtime-evolutionary deep model compression for mobile applications,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Adaspring: Context- adaptive and runtime-evolutionary deep model compression for mobile applications,

Reference 10

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Observation 828f3f20-86ba-45b5-ae1f-e7a3a37c5310 · outbound

This paper cites Adaptive weight compression for memory-efficient neural networks,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Adaptive weight compression for memory-efficient neural networks,

Reference 11

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Observation bf204469-eab7-4e1c-b3af-16f1d15f1dfe · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices 8-bit Optimizers via Block-wise Quantization

Reference 12

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Observation 8d4721a6-2063-4ac3-913e-d8766d1ec2de · outbound

This paper cites MixConv: Mixed Depthwise Convolutional Kernels.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices MixConv: Mixed Depthwise Convolutional Kernels

Reference 13

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Observation 79f52c2a-b0ee-41a8-86d7-a5d3a63aa9d4 · outbound

This paper cites AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments

Reference 14

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This paper cites Inceptionnext: When inception meets convnext,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Inceptionnext: When inception meets convnext,

Reference 15

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Observation 9e01a1bc-535f-4985-8e04-3d5be27b3e49 · outbound

This paper cites Dynamic convolution: Attention over convolution kernels,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Dynamic convolution: Attention over convolution kernels,

Reference 16

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Observation e447b402-607e-4640-8439-9f952335c4f7 · outbound

This paper cites Deep guided attention network for joint denoising and demosaicing in real image,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Deep guided attention network for joint denoising and demosaicing in real image,

Reference 17

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Observation 4300b69b-6ba1-4a9f-953f-ffccffdfb4f1 · outbound

This paper cites Adaknife: Flexible dnn offloading for inference acceleration on heterogeneous mobile devices,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Adaknife: Flexible dnn offloading for inference acceleration on heterogeneous mobile devices,

Reference 18

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Observation 4155a60e-a47f-4bab-a5c4-c0dc6af1c9d7 · outbound

This paper cites Context-aware adaptive surgery: A fast and effective framework for adaptative model partition,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Context-aware adaptive surgery: A fast and effective framework for adaptative model partition,

Reference 19

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Observation 186dd8cc-84a7-4290-afd7-b17851c39faa · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 20

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This paper cites ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Reference 21

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Observation 04a2f2c0-917a-4fbd-98a3-f73d017d87da · outbound

This paper cites Progressive neural architecture search,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Progressive neural architecture search,

Reference 22

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This paper cites DARTS: Differentiable Architecture Search.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices DARTS: Differentiable Architecture Search

Reference 23

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This paper cites Adaptivenet: Post-deployment neural architecture adaptation for diverse edge environments,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Adaptivenet: Post-deployment neural architecture adaptation for diverse edge environments,

Reference 24

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This paper cites Legodnn: block-grained scaling of deep neural networks for mobile vision,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Legodnn: block-grained scaling of deep neural networks for mobile vision,

Reference 25

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Observation a68f4727-3fa1-48c6-a801-3e2e5e80eb98 · outbound

This paper cites Neulens: spatial-based dynamic accel- eration of convolutional neural networks on edge,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Neulens: spatial-based dynamic accel- eration of convolutional neural networks on edge,

Reference 26

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This paper cites Hardware- accelerated platforms and infrastructures for network functions: A survey of enabling technologies and research studies,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Hardware- accelerated platforms and infrastructures for network functions: A survey of enabling technologies and research studies,

Reference 27

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This paper cites Deep residual learning for image recognition,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Deep residual learning for image recognition,

Reference 28

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Observation 3d06d156-5c55-4b72-ad1f-1341e4751f28 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 29

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Observation 1a977706-f418-4387-9e86-065f18a2335d · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 30

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This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 31

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This paper cites Shufflenet: An extremely effi- cient convolutional neural network for mobile devices,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Shufflenet: An extremely effi- cient convolutional neural network for mobile devices,

Reference 32

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This paper cites Going deeper with convolutions,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Going deeper with convolutions,

Reference 33

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This paper cites Rethinking the inception architecture for computer vision,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Rethinking the inception architecture for computer vision,

Reference 34

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This paper cites Ghostnet: More features from cheap operations,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Ghostnet: More features from cheap operations,

Reference 35

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This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 36

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This paper cites Mnasnet: Platform-aware neural architecture search for mobile,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Mnasnet: Platform-aware neural architecture search for mobile,

Reference 37

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This paper cites DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

Reference 38

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This paper cites Deep k-means: Re-training and parameter sharing with harder cluster assign- ments for compressing deep convolutions,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Deep k-means: Re-training and parameter sharing with harder cluster assign- ments for compressing deep convolutions,

Reference 39

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Observation d895a1c6-9522-46d0-a65f-6f511747555b · outbound

This paper cites Adadeep: A usage-driven, automated deep model compression framework for enabling ubiquitous intelligent mobiles,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Adadeep: A usage-driven, automated deep model compression framework for enabling ubiquitous intelligent mobiles,

Reference 40

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This paper cites Enabling latency-sensitive dnn inference via joint optimization of model surgery and resource allocation in heterogeneous edge,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Enabling latency-sensitive dnn inference via joint optimization of model surgery and resource allocation in heterogeneous edge,

Reference 41

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This paper cites Branchynet: Fast inference via early exiting from deep neural networks,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Branchynet: Fast inference via early exiting from deep neural networks,

Reference 42

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Observation dcebb4cf-c9c2-40ea-9bbb-bc468e608c98 · outbound

This paper cites Learning both weights and con- nections for efficient neural network,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Learning both weights and con- nections for efficient neural network,

Reference 43

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This paper cites Nn-stretch: Automatic neural network branching for parallel inference on heterogeneous multi-processors,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Nn-stretch: Automatic neural network branching for parallel inference on heterogeneous multi-processors,

Reference 44

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This paper cites Adaenlight: Energy-aware low-light video stream enhancement on mo- bile devices,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Adaenlight: Energy-aware low-light video stream enhancement on mo- bile devices,

Reference 45

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This paper cites Learning multiple layers of features from tiny images,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Learning multiple layers of features from tiny images,

Reference 46

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Observation 3168085a-345a-421f-bcdf-1a4d0ce05a60 · outbound

This paper cites Learning multiple layers of features from tiny images,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Learning multiple layers of features from tiny images,

Reference 47

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This paper cites Tiny imagenet visual recognition challenge,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Tiny imagenet visual recognition challenge,

Reference 48

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This paper cites Neural Architecture Search with Reinforcement Learning.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Neural Architecture Search with Reinforcement Learning

Reference 49

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This paper cites Attention is all you need,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Attention is all you need,

Reference 50

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This paper cites Tiny Models are the Computational Saver for Large Models.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Tiny Models are the Computational Saver for Large Models

Reference 51

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Observation eec07143-84c9-4d6c-8f7a-a9333cb88b01 · outbound

This paper cites Pmc: A privacy-preserving deep learning model customization framework for edge computing,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Pmc: A privacy-preserving deep learning model customization framework for edge computing,

Reference 52

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Observation f777375b-f8d7-4fa5-9ea9-93039504547f · outbound

This paper cites Distributed deep convolutional neural networks for the internet-of-things,.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Distributed deep convolutional neural networks for the internet-of-things,

Reference 53

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Learning in the wild: When, how, and what to learn for on-device dataset adaptation,

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices A survey of quantization methods for efficient neural network infer- ence,

Reference 55

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 56

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Distilling the Knowledge in a Neural Network

Reference 57

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Con- densenet: An efficient densenet using learned group convolutions,

Reference 58

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Squeezenext: Hardware-aware neural network design,

Reference 59

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices A com- prehensive survey on model compression and acceleration,

Reference 60

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices A survey on evolutionary neural architecture search,

Reference 61

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Edgecompress: Coupling multi-dimensional model compression and dynamic inference for edgeai,

Reference 62

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Latency-Aware Differentiable Neural Architecture Search

Reference 63

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Neuralpower: Predict and deploy energy-efficient convolutional neural networks,

Reference 64

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Chamnet: Towards efficient network design through platform-aware model adaptation,

Reference 65

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices,

Reference 66

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Searching the deployable convolution neural networks for gpus,

Reference 67

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices On-demand deep model compression for mobile devices: A usage-driven model selection framework,

Reference 68

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