Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction | Paper Citation Record

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This paper cites Remote sensing image scene classifi- cation: Benchmark and state of the art,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Remote sensing image scene classifi- cation: Benchmark and state of the art,

Reference 1

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This paper cites Image based techniques for crack detection, classification and quantification in asphalt pavement: a review,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Image based techniques for crack detection, classification and quantification in asphalt pavement: a review,

Reference 2

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This paper cites Efficient acceleration of deep learning inference on resource-constrained edge devices: A review,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Efficient acceleration of deep learning inference on resource-constrained edge devices: A review,

Reference 3

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This paper cites A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities,

Reference 4

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

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 5

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This paper cites Energy efficient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmission,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Energy efficient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmission,

Reference 6

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This paper cites Bi-deepvit: Binarized transformer for efficient sensor-based human activity recognition,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Bi-deepvit: Binarized transformer for efficient sensor-based human activity recognition,

Reference 7

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This paper cites Post-training piecewise linear quantization for deep neural networks,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Post-training piecewise linear quantization for deep neural networks,

Reference 8

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This paper cites Towards accurate post- training network quantization via bit-split and stitching,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Towards accurate post- training network quantization via bit-split and stitching,

Reference 9

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This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 10

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This paper cites Learned step size quantization,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Learned step size quantization,

Reference 11

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This paper cites Herding dynamical weights to learn,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Herding dynamical weights to learn,

Reference 12

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This paper cites Deep learning on a data diet: Finding important examples early in training,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Deep learning on a data diet: Finding important examples early in training,

Reference 13

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This paper cites Grad- match: Gradient matching based data subset selection for efficient deep model training,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Grad- match: Gradient matching based data subset selection for efficient deep model training,

Reference 14

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This paper cites Beyond neural scaling laws: beating power law scaling via data pruning,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Beyond neural scaling laws: beating power law scaling via data pruning,

Reference 15

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Observation 30fe4e67-a3a8-481b-bc84-82fbfc8dff26 · outbound

This paper cites Robust and efficient quantization-aware training via coreset selection,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Robust and efficient quantization-aware training via coreset selection,

Reference 16

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Observation 6f697fe0-b9a3-4cf2-ad21-56a260202c21 · outbound

This paper cites Q-vit: Accurate and fully quantized low-bit vision transformer,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Q-vit: Accurate and fully quantized low-bit vision transformer,

Reference 17

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This paper cites Over- coming oscillations in quantization-aware training,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Over- coming oscillations in quantization-aware training,

Reference 18

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This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 19

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This paper cites Crossvit: Cross-attention multi- scale vision transformer for image classification,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Crossvit: Cross-attention multi- scale vision transformer for image classification,

Reference 20

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This paper cites Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,

Reference 21

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This paper cites Sdq: Stochastic differentiable quantization with mixed precision,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Sdq: Stochastic differentiable quantization with mixed precision,

Reference 22

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This paper cites Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy

Reference 23

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This paper cites Oscillation-free quantization for low-bit vision transformers,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Oscillation-free quantization for low-bit vision transformers,

Reference 24

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This paper cites Moderate coreset: A universal method of data selection for real-world data- efficient deep learning,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Moderate coreset: A universal method of data selection for real-world data- efficient deep learning,

Reference 25

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This paper cites Contextual diversity for active learning,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Contextual diversity for active learning,

Reference 26

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Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Unresolved cited work

Reference 27

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This paper cites An empirical study of example forgetting during deep neural network learning,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction An empirical study of example forgetting during deep neural network learning,

Reference 28

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This paper cites Hard sample matters a lot in zero-shot quantization,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Hard sample matters a lot in zero-shot quantization,

Reference 29

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This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 30

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

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Learning multiple layers of features from tiny images,

Reference 31

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This paper cites Imagenet: A large-scale hierarchical image database,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Imagenet: A large-scale hierarchical image database,

Reference 32

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Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Pytorch: An imperative style, high-performance deep learning library,

Reference 33

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This paper cites Deep residual learning for image recognition,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Deep residual learning for image recognition,

Reference 34

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This paper cites Lsq+: Improving low-bit quantization through learnable offsets and better initialization,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Lsq+: Improving low-bit quantization through learnable offsets and better initialization,

Reference 35

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This paper cites Communication-efficient satellite-ground federated learning through progressive weight quantization,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Communication-efficient satellite-ground federated learning through progressive weight quantization,

Reference 36

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This paper cites Binarized neural network for edge intelligence of sensor-based human activity recognition,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Binarized neural network for edge intelligence of sensor-based human activity recognition,

Reference 37

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This paper cites Adversarial Active Learning for Deep Networks: a Margin Based Approach.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Adversarial Active Learning for Deep Networks: a Margin Based Approach

Reference 38

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This paper cites Active learning by acquiring contrastive examples,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Active learning by acquiring contrastive examples,

Reference 39

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This paper cites Coresets for data- efficient training of machine learning models,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Coresets for data- efficient training of machine learning models,

Reference 40

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Observation 8250a34c-17f7-4d23-b965-15cadb065786 · outbound

This paper cites Object detection with deep learning: A review,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Object detection with deep learning: A review,

Reference 41

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This paper cites Object detection in 20 years: A survey,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Object detection in 20 years: A survey,

Reference 42

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This paper cites Review the state-of- the-art technologies of semantic segmentation based on deep learning,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Review the state-of- the-art technologies of semantic segmentation based on deep learning,

Reference 43

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This paper cites Semantic segmentation using vision transformers: A survey,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Semantic segmentation using vision transformers: A survey,

Reference 44

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 45

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This paper cites Language mod- els are few-shot learners,.

Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction Language mod- els are few-shot learners,

Reference 46

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