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Paper Citation Record · LEDGER

Histogram-Equalized Quantization for logic-gated Residual Neural Networks

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2501.04517.

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pith.paper-citation-record.v1
2501.04517 v2

Coverage vector

measured 38 of 38 reference resolution

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measured 38 of 38 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 8d0fc0a7-facc-4d92-90a8-a38eaf11dba7 · outbound

This paper cites Quantized neural networks: Training neural networks with low pre- cision weights and activations,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Quantized neural networks: Training neural networks with low pre- cision weights and activations,

Reference 1

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Observation 16b491fe-4b07-4102-9dfa-26783cdae794 · outbound

This paper cites UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision,

Reference 2

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This paper cites A 617 TOPS/W All Digital Binary Neural Network Accelerator in 10nm FinFET CMOS,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks A 617 TOPS/W All Digital Binary Neural Network Accelerator in 10nm FinFET CMOS,

Reference 3

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This paper cites A Resource-Efficient Inference Accelerator for Binary Convolutional Neural Networks,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks A Resource-Efficient Inference Accelerator for Binary Convolutional Neural Networks,

Reference 4

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Observation 7d3bd7f4-4db7-49f0-a2fb-5e103517fceb · outbound

This paper cites Chewbaccann: A flexible 223 tops/w bnn accelerator,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Chewbaccann: A flexible 223 tops/w bnn accelerator,

Reference 5

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This paper cites A convolutional neu- ral network accelerator architecture with fine-granular mixed precision configurability,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks A convolutional neu- ral network accelerator architecture with fine-granular mixed precision configurability,

Reference 6

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This paper cites Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,

Reference 7

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This paper cites Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,

Reference 8

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Observation d15b8c51-b19f-4613-a41a-0df2aba16773 · outbound

This paper cites Convolutional Neural Networks using Logarithmic Data Representation.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Convolutional Neural Networks using Logarithmic Data Representation

Reference 9

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Observation b2e683c4-4756-4a00-b731-291dadcbd3cf · outbound

This paper cites LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

Reference 10

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Observation de2c0520-d1a0-4e94-bbd7-97f3a8715e56 · outbound

This paper cites Linear symmetric quantization of neural networks for low-precision integer hardware,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Linear symmetric quantization of neural networks for low-precision integer hardware,

Reference 11

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Observation b43723d5-ffed-4798-a824-8919a893c7c9 · outbound

This paper cites Ternary Weight Networks.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Ternary Weight Networks

Reference 12

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This paper cites Trained ternary quantization,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Trained ternary quantization,

Reference 13

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This paper cites TRQ: Ternary Neural Networks With Residual Quantization,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks TRQ: Ternary Neural Networks With Residual Quantization,

Reference 14

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Learned step size quantization,

Reference 15

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Observation 8f3581f8-6c1d-4e0f-9913-5775f99491f8 · outbound

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

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 16

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Observation cfb78390-e7f2-44f1-afdc-fdc6994c7f98 · outbound

This paper cites Mixed precision DNNs: All you need is a good parametrization,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Mixed precision DNNs: All you need is a good parametrization,

Reference 17

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Observation 08e553e9-1c25-4632-9804-795ff55a6d14 · outbound

This paper cites Adaptive Quantization Method for CNN with Computational-Complexity-Aware Regularization,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Adaptive Quantization Method for CNN with Computational-Complexity-Aware Regularization,

Reference 18

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks A mathematical theory of communication,

Reference 19

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Effective Quantization Methods for Recurrent Neural Networks

Reference 20

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Observation 6bc9f98f-f855-4853-848a-b7345b9a2aa8 · outbound

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Effective Quantization Approaches for Recurrent Neural Networks,

Reference 21

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Observation d85b5fa0-e78c-42e6-b32b-59da2acfa5fa · outbound

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

Histogram-Equalized Quantization for logic-gated Residual Neural Networks DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 22

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Observation f6747356-a958-464c-a582-b4dc4ece57f8 · outbound

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Learning Multiple Layers of Features from Tiny Images,

Reference 23

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Observation 96a89fa7-1777-46e3-af94-a88b4d89fc66 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 24

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Soft threshold ternary networks,

Reference 25

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Deep residual learning for image recognition,

Reference 26

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm,

Reference 27

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Mobinet: A mobile binary network for image classification,

Reference 28

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Hawqv3: Dyadic neural network quantization,

Reference 29

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Observation 79c69762-cb3c-4ace-b4a5-9d36b99dd7d3 · outbound

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Residual attention network for image classification,

Reference 30

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Very deep convolutional networks for large-scale image recognition,

Reference 31

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks Embedded floating-point units in FPGAs,

Reference 32

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Observation d442ee5e-1e29-415c-b91a-92812c0645e9 · outbound

This paper cites Computing’s energy problem (and what we can do about it),.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Computing’s energy problem (and what we can do about it),

Reference 33

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Observation ea1e917d-bd67-4646-a98e-c727589328e4 · outbound

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Histogram-Equalized Quantization for logic-gated Residual Neural Networks An analysis of single-layer networks in unsupervised feature learning,

Reference 34

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Observation f8853236-ba69-48b9-b4d5-392de4b1fd6b · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Improved Regularization of Convolutional Neural Networks with Cutout

Reference 35

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Observation 6de56dde-2294-402c-9a5d-f3e535bb927c · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 36

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unresolved
no resolver link, observed 2026-08-10T21:37:20.538492Z

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Observation fdbaabf1-fd3f-4b5c-a9cc-09bf9e85952c · outbound

This paper cites Larq: An open-source library for training binarized neural networks,.

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Larq: An open-source library for training binarized neural networks,

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-10T21:37:21.217351Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 86906cd5-0756-490c-bd17-dc6daa8243bc · outbound

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

Histogram-Equalized Quantization for logic-gated Residual Neural Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2015

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no resolver link, observed 2026-08-10T21:37:20.478501Z

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Pith citing papers

No inbound Pith citation observations are available.