Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression | Paper Citation Record

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This paper cites an unresolved cited work.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Unresolved cited work

Reference 1

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This paper cites Adam: Adaptive ap proximate multiplier for fault tolerance in dnn accelerators,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Adam: Adaptive ap proximate multiplier for fault tolerance in dnn accelerators,

Reference 2

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This paper cites T ransaxx: Efﬁcient transformers with approximate computing,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression T ransaxx: Efﬁcient transformers with approximate computing,

Reference 3

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This paper cites Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

Reference 4

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This paper cites NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search

Reference 5

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This paper cites Communication-Efficient Federated Learning via Clipped Uniform Quantization.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Communication-Efficient Federated Learning via Clipped Uniform Quantization

Reference 6

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This paper cites Quantizat ion without tears,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Quantizat ion without tears,

Reference 7

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This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 8

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This paper cites Efficient CNNs via Passive Filter Pruning.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Efficient CNNs via Passive Filter Pruning

Reference 9

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This paper cites Consecutive layer collaborati ve ﬁlter similarity for differentiable neural network pruning,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Consecutive layer collaborati ve ﬁlter similarity for differentiable neural network pruning,

Reference 10

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This paper cites Pruning convolution neural n etworks using ﬁlter clustering based on normalized cross-correlat ion similarity,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Pruning convolution neural n etworks using ﬁlter clustering based on normalized cross-correlat ion similarity,

Reference 11

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This paper cites Losparse: Structured compression of large language model s based on low-rank and sparse approximation,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Losparse: Structured compression of large language model s based on low-rank and sparse approximation,

Reference 12

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This paper cites Low-Rank Matrix Approximation for Neural Network Compression.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Low-Rank Matrix Approximation for Neural Network Compression

Reference 13

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This paper cites Uncertai nty-based knowledge distillation for bayesian deep neural network co mpression,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Uncertai nty-based knowledge distillation for bayesian deep neural network co mpression,

Reference 14

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This paper cites Counterclockwise block-by-block knowledge distillatio n for neural network compression,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Counterclockwise block-by-block knowledge distillatio n for neural network compression,

Reference 15

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This paper cites A compre hensive survey on model quantization for deep neural networks in ima ge classiﬁcation,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression A compre hensive survey on model quantization for deep neural networks in ima ge classiﬁcation,

Reference 16

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This paper cites Hfpq: deep neural network com pression by hardware-friendly pruning-quantization,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Hfpq: deep neural network com pression by hardware-friendly pruning-quantization,

Reference 17

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This paper cites Hardware-aware dnn compression via diverse prun- ing and mixed-precision quantization,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Hardware-aware dnn compression via diverse prun- ing and mixed-precision quantization,

Reference 18

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This paper cites Optimized convolutional ne ural network at the iot edge for image detection using pruning and quantiz ation,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Optimized convolutional ne ural network at the iot edge for image detection using pruning and quantiz ation,

Reference 19

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This paper cites Filter pruning via geometric median for deep convolutional neural networks ac celeration,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Filter pruning via geometric median for deep convolutional neural networks ac celeration,

Reference 20

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This paper cites Differentiable joi nt pruning and quantization for hardware efﬁciency,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Differentiable joi nt pruning and quantization for hardware efﬁciency,

Reference 21

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

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Learning both wei ghts and con- nections for efﬁcient neural network,

Reference 22

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This paper cites Sparse optimizatio n guided pruning for neural networks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Sparse optimizatio n guided pruning for neural networks,

Reference 23

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This paper cites Thinet: A ﬁlter level pruni ng method for deep neural network compression,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Thinet: A ﬁlter level pruni ng method for deep neural network compression,

Reference 24

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This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 25

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This paper cites A novel and efﬁcient model pruning method for deep convolutional ne ural networks by evaluating the direct and indirect effects of ﬁl ters,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression A novel and efﬁcient model pruning method for deep convolutional ne ural networks by evaluating the direct and indirect effects of ﬁl ters,

Reference 26

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This paper cites Daar: Dual attention coope rative adaptive pruning rate by data-driven for ﬁlter pruning: S. lian et al.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Daar: Dual attention coope rative adaptive pruning rate by data-driven for ﬁlter pruning: S. lian et al

Reference 27

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This paper cites Eacp: An effec tive automatic channel pruning for neural networks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Eacp: An effec tive automatic channel pruning for neural networks,

Reference 28

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This paper cites Pro gressive local ﬁlter pruning for image retrieval acceleration,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Pro gressive local ﬁlter pruning for image retrieval acceleration,

Reference 29

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This paper cites Sfp: Similarity-based ﬁlter pruning for deep neural networks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Sfp: Similarity-based ﬁlter pruning for deep neural networks,

Reference 30

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This paper cites Convolutional Neural Networks using Logarithmic Data Representation.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Convolutional Neural Networks using Logarithmic Data Representation

Reference 31

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This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 32

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This paper cites PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs

Reference 33

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This paper cites QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

Reference 34

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This paper cites Block and subword-s caling ﬂoating-point (BSFP) : An efﬁcient non-uniform quantizati on for low precision inference,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Block and subword-s caling ﬂoating-point (BSFP) : An efﬁcient non-uniform quantizati on for low precision inference,

Reference 35

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This paper cites PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation

Reference 36

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This paper cites Compressed neural ar- chitecture utilizing dimensionality reduction and quanti zation,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Compressed neural ar- chitecture utilizing dimensionality reduction and quanti zation,

Reference 37

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This paper cites Quantization-aware trainin g with dynamic and static pruning,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Quantization-aware trainin g with dynamic and static pruning,

Reference 38

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This paper cites Non-structured dnn weight pruning—is it beneﬁcial in any platform?.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Non-structured dnn weight pruning—is it beneﬁcial in any platform?

Reference 39

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This paper cites Deep residual learni ng for image recognition,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Deep residual learni ng for image recognition,

Reference 40

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

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 41

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

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Learning multiple layers of features from tiny images,

Reference 42

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This paper cites Blen ded coarse gradient descent for full quantization of deep neural netwo rks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Blen ded coarse gradient descent for full quantization of deep neural netwo rks,

Reference 43

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Observation e2b426f8-ecba-455c-9885-86d0bc28ef5d · outbound

This paper cites Ro bustness- aware 2-bit quantization with real-time performance for ne ural network,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Ro bustness- aware 2-bit quantization with real-time performance for ne ural network,

Reference 44

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Observation ff5bf210-c361-41f0-a1c6-df47a259b1b0 · outbound

This paper cites Se arching for low-bit weights in quantized neural networks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Se arching for low-bit weights in quantized neural networks,

Reference 45

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Observation daec8255-8787-40f7-9ac0-718e53407332 · outbound

This paper cites Search w hat you want: Barrier panelty nas for mixed precision quantization ,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Search w hat you want: Barrier panelty nas for mixed precision quantization ,

Reference 46

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This paper cites Dynamical channel pruning by conditional accuracy change for deep neural netw orks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Dynamical channel pruning by conditional accuracy change for deep neural netw orks,

Reference 47

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Observation a2e778de-6a48-4dac-b630-f7e3e3bd1457 · outbound

This paper cites Iterative clus tering pruning for convolutional neural networks,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Iterative clus tering pruning for convolutional neural networks,

Reference 48

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Observation eb9e6d3a-4333-4a46-92b5-adeb1781fcd5 · outbound

This paper cites Hessian-aware pruning and optimal neural impl ant,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Hessian-aware pruning and optimal neural impl ant,

Reference 49

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Observation c51b1efe-0a1b-4d28-8497-f82c8bd70425 · outbound

This paper cites Concurrent Training and Layer Pruning of Deep Neural Networks.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Concurrent Training and Layer Pruning of Deep Neural Networks

Reference 50

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Observation 8abb2e95-ffdd-4d99-8f66-f7446a40b51f · outbound

This paper cites Shallowing deep networks: Layer-w ise pruning based on feature representations,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Shallowing deep networks: Layer-w ise pruning based on feature representations,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:19:04.682967Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2d2744c-7663-476d-b8a1-4b56f15668df · outbound

This paper cites Inference- aware convolutional neural network pruning,.

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression Inference- aware convolutional neural network pruning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:19:04.666698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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