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Paper Citation Record · LEDGER

Efficient Deep Neural Networks

As of 14 August 2026, this Paper Citation Record lists 100 of 181 outbound references and 0 inbound Pith citation observations for arXiv:1908.08926.

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pith.paper-citation-record.v1
1908.08926 v1

Coverage vector

measured 100 of 181 reference resolution

Typed states for the displayed outbound observations.

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measured 100 of 100 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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Source: cited_works

Reference resolution

100 of 181 outbound references displayed

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External citation measurements

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Outbound references

Observation 2ec01566-f5d1-42c4-a2f4-8b3ccc48bf66 · outbound

This paper cites The Vapnik-Chervonenkis dimension: Information versus com- plexity in learning.

Efficient Deep Neural Networks The Vapnik-Chervonenkis dimension: Information versus com- plexity in learning

Reference 1

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Observation 2711d48e-0bb0-4dfb-9df3-f4d2a226a710 · outbound

This paper cites Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++.

Efficient Deep Neural Networks Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

Reference 2

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Observation dfdac729-99f2-4243-b36c-d24625ab6d94 · outbound

This paper cites Shallow Networks for High-Accuracy Road Object-Detection.

Efficient Deep Neural Networks Shallow Networks for High-Accuracy Road Object-Detection

Reference 3

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Observation 604d3f5b-ede4-4ab7-9f78-49dc714c6e78 · outbound

This paper cites Label Refinery: Improving ImageNet Classification through Label Progression.

Efficient Deep Neural Networks Label Refinery: Improving ImageNet Classification through Label Progression

Reference 4

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Observation 69a3cff5-9b51-4da0-8792-ecef25b0932c · outbound

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

Efficient Deep Neural Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

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Observation 6112699b-341a-4a69-a3fe-4a2c344b5141 · outbound

This paper cites FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks.

Efficient Deep Neural Networks FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks

Reference 6

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Observation edd9de93-8c1c-47aa-9794-8313452339c5 · outbound

This paper cites Unsupervised pixel-level domain adaptation with gen- erative adversarial networks.

Efficient Deep Neural Networks Unsupervised pixel-level domain adaptation with gen- erative adversarial networks

Reference 7

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Observation b72991b5-d75c-4924-9368-eb8062ef3ea6 · outbound

This paper cites A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection.

Efficient Deep Neural Networks A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

Reference 8

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Observation 5de02892-1a12-4bfd-bfdd-331a281bec2c · outbound

This paper cites Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks.

Efficient Deep Neural Networks Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

Reference 9

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Observation c3839e3b-a5dc-4747-9b23-73a950ed4a51 · outbound

This paper cites Annotating object instances with a polygon-rnn.

Efficient Deep Neural Networks Annotating object instances with a polygon-rnn

Reference 10

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Observation fd014703-6adf-452d-b1a4-a5027f03553a · outbound

This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Efficient Deep Neural Networks DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Reference 11

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Observation c2ac8184-7c16-4556-af0f-9b905b02855f · outbound

This paper cites All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification.

Efficient Deep Neural Networks All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification

Reference 12

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Observation 751a8dd8-1c7f-4a85-bbd8-96d49e2f81fa · outbound

This paper cites Multi-View 3D Object Detection Network for Autonomous Driving.

Efficient Deep Neural Networks Multi-View 3D Object Detection Network for Autonomous Driving

Reference 13

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Observation cef7af58-330c-4e7a-8e0a-2d4b483eaf1f · outbound

This paper cites DetNAS: Backbone Search for Object Detection.

Efficient Deep Neural Networks DetNAS: Backbone Search for Object Detection

Reference 14

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Observation c6557833-1db6-4921-85ee-9275cb216349 · outbound

This paper cites cuDNN: Efficient Primitives for Deep Learning.

Efficient Deep Neural Networks cuDNN: Efficient Primitives for Deep Learning

Reference 15

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Observation d51e9ec8-f881-4df0-b22e-08e2c21ca1f3 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Efficient Deep Neural Networks PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 16

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Observation 179e128a-3d4b-44aa-85f8-1ca68d9d5f49 · outbound

This paper cites Xception: Deep Learning with Depthwise Separable Convolutions.

Efficient Deep Neural Networks Xception: Deep Learning with Depthwise Separable Convolutions

Reference 17

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Observation fd9bc8c2-2140-4176-8fb9-6e956c4da554 · outbound

This paper cites Visual Wake Words Dataset.

Efficient Deep Neural Networks Visual Wake Words Dataset

Reference 18

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Observation 9d4e72c8-0a56-4bf1-8815-eebec2c0ccb8 · outbound

This paper cites Domain Adaptation for Visual Applications: A Comprehensive Survey.

Efficient Deep Neural Networks Domain Adaptation for Visual Applications: A Comprehensive Survey

Reference 19

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Observation 6d0d0ac1-d69e-49d9-aeda-d405680392e0 · outbound

This paper cites Histograms of Oriented Gradients for Human Detec- tion.

Efficient Deep Neural Networks Histograms of Oriented Gradients for Human Detec- tion

Reference 20

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Observation 49b7c034-8b23-4410-82c5-aad3bc04d7df · outbound

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

Efficient Deep Neural Networks Imagenet: A large-scale hierarchical image database

Reference 21

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This paper cites HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision.

Efficient Deep Neural Networks HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision

Reference 22

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Efficient Deep Neural Networks CARLA: An Open Urban Driving Simulator

Reference 23

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Efficient Deep Neural Networks On the segmentation of 3D LIDAR point clouds

Reference 24

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Efficient Deep Neural Networks On the segmentation of 3D LIDAR point clouds

Reference 25

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Efficient Deep Neural Networks Dutta, A

Reference 26

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This paper cites A Density-based Algorithm for Discovering Clusters a Density- based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.

Efficient Deep Neural Networks A Density-based Algorithm for Discovering Clusters a Density- based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

Reference 27

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Observation 03f52c2c-c976-4f05-85d8-0a1e43a21038 · outbound

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Efficient Deep Neural Networks The Pascal Visual Object Classes (VOC) Challenge

Reference 28

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Efficient Deep Neural Networks Object detection with discriminatively trained part- based models

Reference 29

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Efficient Deep Neural Networks Scenic: a language for scenario specification and scene gen- eration

Reference 30

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Efficient Deep Neural Networks Domain-adversarial training of neural networks

Reference 31

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Efficient Deep Neural Networks A Neural Algorithm of Artistic Style

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Observation 28dc2a53-05c5-43ca-a1be-7f7f2d9c24fd · outbound

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Efficient Deep Neural Networks Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 33

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Observation e29c0250-b225-4c9e-90f3-c7dfdd4cfdeb · outbound

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Efficient Deep Neural Networks Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 34

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Observation feb316e7-fad1-4729-93f7-b295e32170d5 · outbound

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Efficient Deep Neural Networks Deep reconstruction-classification networks for unsuper- vised domain adaptation

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Efficient Deep Neural Networks Domain generalization for object recognition with multi- task autoencoders

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Efficient Deep Neural Networks SqueezeNext: Hardware-Aware Neural Network Design

Reference 37

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Efficient Deep Neural Networks Fast R-CNN

Reference 38

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Observation 49011878-abce-4509-b061-55935cd5204c · outbound

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Efficient Deep Neural Networks Deformable Part Models are Convolutional Neural Networks

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Observation 2b80f77a-2092-442f-9e71-52a145b82811 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Efficient Deep Neural Networks Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 40

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Observation b6956c08-3538-4ba1-a461-64516ecac71f · outbound

This paper cites Supplementary Material: Rich feature hierarchies for accurate object detection and semantic segmentation.

Efficient Deep Neural Networks Supplementary Material: Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 41

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Observation a487f029-b190-4ca6-8e72-7351091ed32a · outbound

This paper cites Software-Hardware Codesign for Efficient Neural Network Ac- celeration.

Efficient Deep Neural Networks Software-Hardware Codesign for Efficient Neural Network Ac- celeration

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Observation f956af2d-2d4e-4a01-a31b-9848fa13cc7c · outbound

This paper cites Ms-celeb-1m: Challenge of recognizing one million celebrities in the real world.

Efficient Deep Neural Networks Ms-celeb-1m: Challenge of recognizing one million celebrities in the real world

Reference 43

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Observation 3e5a560c-1d32-4f67-91d7-abb5efb40731 · outbound

This paper cites Single Path One-Shot Neural Architecture Search with Uniform Sampling.

Efficient Deep Neural Networks Single Path One-Shot Neural Architecture Search with Uniform Sampling

Reference 44

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Observation 56933147-b87c-487c-8ea7-8c7d88c916a2 · outbound

This paper cites The unreasonable effectiveness of data.

Efficient Deep Neural Networks The unreasonable effectiveness of data

Reference 45

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Observation e874090a-36f6-4753-8d3d-29db09332d27 · outbound

This paper cites Deep Compression: Compressing DNNs with Pruning, Trained Quantization and Huffman Coding.

Efficient Deep Neural Networks Deep Compression: Compressing DNNs with Pruning, Trained Quantization and Huffman Coding

Reference 46

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Observation 197d12ff-dc88-4194-9b80-895993347ea1 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Efficient Deep Neural Networks Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 47

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source=pdf_text observed=2026-08-14T12:19:53.458539Z digest=sha256:a99537cc142297c8e1b43f3e6ff951c7f535036ff45eb8028fd4951d1e8457e2

Observation 36396b45-d17a-402b-9ed8-d682298078f5 · outbound

This paper cites Achieving Human Parity on Automatic Chinese to English News Translation.

Efficient Deep Neural Networks Achieving Human Parity on Automatic Chinese to English News Translation

Reference 48

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source=pdf_text observed=2026-08-14T12:19:53.463873Z digest=sha256:ddc772363d5212166af62914b6fc0393899f2934b837b3ef98d392066caa7b25

Observation af56d365-1384-438e-8345-ff44724bf29b · outbound

This paper cites Deep Residual Learning for Image Recognition.

Efficient Deep Neural Networks Deep Residual Learning for Image Recognition

Reference 49

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source=pdf_text observed=2026-08-14T12:19:53.468969Z digest=sha256:f533037718fb3881f52ce06987fe161064b0262ed20b4f7f01371ebbbbb5950b

Observation 71cda0b5-7f68-4dd7-bb50-8566dce0d84c · outbound

This paper cites Deep residual learning for image recognition.

Efficient Deep Neural Networks Deep residual learning for image recognition

Reference 50

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source=pdf_text observed=2026-08-14T12:19:53.475038Z digest=sha256:19055d4be07ddc17f2685e78ccf64f66142845140a742b6c6efabe7e339b9b4e

Observation e8787b2d-3823-4a1d-aea1-609df6288973 · outbound

This paper cites Identity mappings in deep residual networks.

Efficient Deep Neural Networks Identity mappings in deep residual networks

Reference 51

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source=pdf_text observed=2026-08-14T12:19:53.480147Z digest=sha256:cdf6589b6859b902e04194c55cf4246b19a8f3ed16863c6b3640175655d480ec

Observation f85c0c5e-51c0-4125-8b73-f4c89b9e686e · outbound

This paper cites Mask r-cnn.

Efficient Deep Neural Networks Mask r-cnn

Reference 52

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source=pdf_text observed=2026-08-14T12:19:53.484941Z digest=sha256:55b3efcc54ce453435b240b2ca8c0bf5fd955f0271acae715e41146cd07246e8

Observation 869450c0-1917-4c25-a439-df51a21c274b · outbound

This paper cites Addressnet: Shift-based primitives for efficient convolutional neural networks.

Efficient Deep Neural Networks Addressnet: Shift-based primitives for efficient convolutional neural networks

Reference 53

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source=pdf_text observed=2026-08-14T12:19:53.489759Z digest=sha256:6f1c2cb59c961f2ad717992e8243441192ea382f3833058fc5e3b063d7d9d336

Observation de81de27-6881-4180-b522-6f5b6492c093 · outbound

This paper cites AMC: AutoML for Model Compression and Acceleration on Mobile Devices.

Efficient Deep Neural Networks AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Reference 54

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source=pdf_text observed=2026-08-14T12:19:53.494547Z digest=sha256:3be38fc7276b1f8e313d64ab8456ef66e24df22108104c1e282e54ab7734b076

Observation 60e528f8-c534-477c-9b30-a5940d824db0 · outbound

This paper cites LIDAR-based 3D object perception.

Efficient Deep Neural Networks LIDAR-based 3D object perception

Reference 55

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source=pdf_text observed=2026-08-14T12:19:53.499752Z digest=sha256:dcb02f1ad66506ef93c31457ca4c88974144f8dc33b3ed188b7712b22fdadf96

Observation 6be9eea1-96af-4da5-b3e9-bc1cf0e3601b · outbound

This paper cites CyCADA: Cycle-Consistent Adversarial Domain Adaptation.

Efficient Deep Neural Networks CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Reference 56

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source=pdf_text observed=2026-08-14T12:19:53.504754Z digest=sha256:d58852b2c84850c15cdc84d19c5d7efd233d95a39f16264897f70d25462c821b

Observation c53cac01-349d-49b1-82b1-fc038ff28885 · outbound

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

Efficient Deep Neural Networks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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source=pdf_text observed=2026-08-14T12:19:53.516223Z digest=sha256:925aeef4ff3f88e73f623feb3e50b9b3009b3b6301ee77ce83e7caf6f315e6cc

Observation 611672a4-baa9-43a5-b5dc-2a6af153f9c1 · outbound

This paper cites Searching for MobileNetV3.

Efficient Deep Neural Networks Searching for MobileNetV3

Reference 59

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source=pdf_text observed=2026-08-14T12:19:53.521579Z digest=sha256:25107def3cd79b07492ab28a0a439592f0dcb71a22fe2a154e088060e621f118

Observation 0a7b0bdb-94bb-42de-bea5-e00597206775 · outbound

This paper cites Squeeze-and-excitation networks.

Efficient Deep Neural Networks Squeeze-and-excitation networks

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source=pdf_text observed=2026-08-14T12:19:53.534338Z digest=sha256:44fc4feb3aec464ee5fa2b3d2d28c254d1e4c635567c2bd0dd9520f2a99732c0

Observation 87db7b75-88b3-4d4d-933a-0f76acce4d36 · outbound

This paper cites Densely Connected Convolutional Networks.

Efficient Deep Neural Networks Densely Connected Convolutional Networks

Reference 61

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source=pdf_text observed=2026-08-14T12:19:53.539688Z digest=sha256:e7e0326d0a7b49bbff4a5b42b3b90375f46329a7174c1debc6e2ced3e6d34a15

Observation 5e6225bd-009f-48de-be2b-610a760b0090 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Efficient Deep Neural Networks Rethinking the inception architecture for computer vision

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source=pdf_text observed=2026-08-14T12:19:53.544490Z digest=sha256:aca0d20439e4730bcd6bd8fa2d3cf3a01b987ccdb298713d3e2a26f2c1ebc98b

Observation 7f3d99e3-b8fe-4eec-94ae-4487644ce94d · outbound

This paper cites The apolloscape dataset for autonomous driving.

Efficient Deep Neural Networks The apolloscape dataset for autonomous driving

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source=pdf_text observed=2026-08-14T12:19:53.550345Z digest=sha256:dbedae751fd02fb817fd87e879db6088a1371031754606796024e9881a70d8ab

Observation 948bc508-43f5-40b8-90f6-364c4d08aaff · outbound

This paper cites DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.

Efficient Deep Neural Networks DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

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source=pdf_text observed=2026-08-14T12:19:53.555866Z digest=sha256:4bb81e10014d3d1c65f5d33f020ff903e315dfb88c09dca090888063f70819cd

Observation ec95d0c1-feff-4cb6-9c0b-559a27ccfc31 · outbound

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

Efficient Deep Neural Networks SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

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source=pdf_text observed=2026-08-14T12:19:53.566440Z digest=sha256:72bedf4446749e42a599aab06cb49050446cea8a62b8a3c21625788e4b04146e

Observation 518bfc4b-f1b6-497a-ad54-d5bd35134f9e · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Efficient Deep Neural Networks Batch normalization: Accelerating deep network training by reducing internal covariate shift

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source=pdf_text observed=2026-08-14T12:19:53.571543Z digest=sha256:1d6df7229d6924176afe83a313715bbdd8d7b860810ab9614683ecb2383126d8

Observation add08769-768e-4c9a-9dde-878b670bca6a · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Efficient Deep Neural Networks Categorical Reparameterization with Gumbel-Softmax

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source=pdf_text observed=2026-08-14T12:19:53.576669Z digest=sha256:552932cd8217f744885a71acc010bf49c63069a99b19c0b4a61a6383d6a18781

Observation 7ba52d64-782a-4b9c-9644-2159f65e8f5c · outbound

This paper cites Caffe: Convolutional Architecture for Fast Feature Embedding.

Efficient Deep Neural Networks Caffe: Convolutional Architecture for Fast Feature Embedding

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source=pdf_text observed=2026-08-14T12:19:53.581656Z digest=sha256:da47ec13992cd9a721d838d6b650ff109fd894195356eac38eb00d2907ad9fd3

Observation 41cde180-6434-441f-bb4c-75699c579f73 · outbound

This paper cites Accelerating low bit-width convolutional neural networks with em- bedded FPGA.

Efficient Deep Neural Networks Accelerating low bit-width convolutional neural networks with em- bedded FPGA

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source=pdf_text observed=2026-08-14T12:19:53.586287Z digest=sha256:b92da26b5e5a646daec0377ceff2f3d2d1e6543a89c330c0da026caa8557005a

Observation 50715c44-eb90-4abf-af38-9664ec17ceed · outbound

This paper cites Perceptual Losses for Real-Time Style Transfer and Super-Resolution.

Efficient Deep Neural Networks Perceptual Losses for Real-Time Style Transfer and Super-Resolution

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source=pdf_text observed=2026-08-14T12:19:53.591332Z digest=sha256:b393780c976e2d43dabe343bbd1452a051e974e9db83ca426098927602cdb3e6

Observation e9824cd3-55c1-4512-88e1-6fab87875955 · outbound

This paper cites Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?.

Efficient Deep Neural Networks Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

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source=pdf_text observed=2026-08-14T12:19:53.596401Z digest=sha256:e433dc51ef39af3f01295777b71beb67c3839103a17121725a6c4efb3180761a

Observation 6b07934d-41bb-46c3-a267-7869b6c6668e · outbound

This paper cites Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?.

Efficient Deep Neural Networks Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?

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source=pdf_text observed=2026-08-14T12:19:53.602134Z digest=sha256:0b09c673c6f4c34e7101db4faec49eaac5ebe32705440cba64b0fdf2140148bb

Observation 9b1ff8c6-6f8e-45ea-84b3-b9f48d8b4268 · outbound

This paper cites Local Binary Convolutional Neural Networks.

Efficient Deep Neural Networks Local Binary Convolutional Neural Networks

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source=pdf_text observed=2026-08-14T12:19:53.606975Z digest=sha256:0bd4b413a3a6390a43967d1db4a304ea40fb7034170146dfb46c3b5771717168

Observation 8d4a21af-f7f7-49cc-8961-b5b9ca6a6223 · outbound

This paper cites Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss.

Efficient Deep Neural Networks Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

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source=pdf_text observed=2026-08-14T12:19:53.611892Z digest=sha256:4f29473b1063368d4c35cecedf3e08adaf8ebab6c094e7b6bc68f7b63596e52b

Observation b85881c2-0895-4820-b4f5-3a9d63340106 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Efficient Deep Neural Networks Adam: A Method for Stochastic Optimization

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source=pdf_text observed=2026-08-14T12:19:53.616787Z digest=sha256:89e77f695d6a818d5d3d1f278308a4ed12fa69754138b1a2d1a585b11c3b0454

Observation 862dd1f3-f6f7-4873-94ae-4857ce5a4be2 · outbound

This paper cites Free supervision from video games.

Efficient Deep Neural Networks Free supervision from video games

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source=pdf_text observed=2026-08-14T12:19:53.621277Z digest=sha256:3434586fd1bfaae548bcea08c2389e07fb481be40b052ddf27466451a8bc2910

Observation 1e1131b0-c1f1-4a60-a815-f5673577e129 · outbound

This paper cites Efficient inference in fully connected crfs with gaussian edge potentials.

Efficient Deep Neural Networks Efficient inference in fully connected crfs with gaussian edge potentials

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source=pdf_text observed=2026-08-14T12:19:53.625762Z digest=sha256:7aae22d4f4b84335b56392c55b4731822e25ddc21903d9c46c49cd4638cc29f8

Observation 232a1255-efff-4487-ae38-81bbf3e2d003 · outbound

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

Efficient Deep Neural Networks Learning multiple layers of features from tiny images

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source=pdf_text observed=2026-08-14T12:19:53.632080Z digest=sha256:6c58dd9ad046de4a7ba7b3ee19ec62c4606bc7d0f5853b79045bd0a857a41697

Observation b45b4d12-de28-4531-959c-25006dcbb4ba · outbound

This paper cites ImageNet Classification with Deep Convolutional Neural Networks.

Efficient Deep Neural Networks ImageNet Classification with Deep Convolutional Neural Networks

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source=pdf_text observed=2026-08-14T12:19:53.637204Z digest=sha256:dc026dce56d97b9f47812c6ca601539d1e106a36cdd6cc5eb6ae4eedb587cba5

Observation 10b897b6-a939-490f-9397-301cbf6eb469 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Efficient Deep Neural Networks Imagenet classification with deep convolutional neural networks

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source=pdf_text observed=2026-08-14T12:19:53.642831Z digest=sha256:77e755399948637d51e4fd522b437236a39ee16ac2b0a98dadb56e08483da2ef

Observation 0667944b-87ec-4236-ac27-c5817e81fa5b · outbound

This paper cites Maestro: A Memory-on-Logic Architecture for Coordinated Parallel Use of Many Systolic Arrays.

Efficient Deep Neural Networks Maestro: A Memory-on-Logic Architecture for Coordinated Parallel Use of Many Systolic Arrays

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source=pdf_text observed=2026-08-14T12:19:53.650101Z digest=sha256:c486a57506c6c548624ad4860c94b4f6e760f0b50e5413855aaea865cf0ee673

Observation 458fae54-bdb7-4c9b-a774-a9e0225ec4f7 · outbound

This paper cites Research methods in human-computer interaction.

Efficient Deep Neural Networks Research methods in human-computer interaction

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Observation 95e64fd7-1bda-4a2d-b620-9e31f071811e · outbound

This paper cites Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM.

Efficient Deep Neural Networks Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM

Reference 84

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source=pdf_text observed=2026-08-14T12:19:53.660599Z digest=sha256:a79c806951146a9b02a8ebc780214ef2d5b349b146dd30f76eb81451232ff965

Observation 5bd5b1f6-16ac-45c3-8d0b-2ce30f5250af · outbound

This paper cites Vehicle Detection from 3D Lidar Using Fully Convolutional Network.

Efficient Deep Neural Networks Vehicle Detection from 3D Lidar Using Fully Convolutional Network

Reference 85

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Observation 2917963e-4648-4b3c-a24e-6844920888ab · outbound

This paper cites Adaptive Batch Normalization for practical domain adaptation.

Efficient Deep Neural Networks Adaptive Batch Normalization for practical domain adaptation

Reference 86

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source=pdf_text observed=2026-08-14T12:19:53.671154Z digest=sha256:bb1eb8b7a7b1eac928bf66464d5fb64d2591d2da53ab52e2ebedc2b95a5b7906

Observation 0d1e2cac-9565-4188-972d-d917e62a8751 · outbound

This paper cites Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages.

Efficient Deep Neural Networks Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

Reference 87

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source=pdf_text observed=2026-08-14T12:19:53.677197Z digest=sha256:83e5c5506ee409b5eb03b191790c6e7bceb91357e08c798e12dc3e856899e577

Observation ea02eb2b-24ea-44d5-a70f-bdb15c37770d · outbound

This paper cites FP-BNN: Binarized neural network on FPGA.

Efficient Deep Neural Networks FP-BNN: Binarized neural network on FPGA

Reference 88

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source=pdf_text observed=2026-08-14T12:19:53.688853Z digest=sha256:f1c847f9ec5b3364f77fbde5e1b013343ed5e744ca64272979a5625f4d3c4f75

Observation 7a6b8673-7b60-433b-996f-40bc427bd64c · outbound

This paper cites TSM: Temporal Shift Module for Efficient Video Understanding.

Efficient Deep Neural Networks TSM: Temporal Shift Module for Efficient Video Understanding

Reference 89

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source=pdf_text observed=2026-08-14T12:19:53.694910Z digest=sha256:0c55f98c8ed431c91b38cb384261f8501990357cf42ef5fb989ffd1574219209

Observation cb110077-be60-4f87-b9fc-326559bffcce · outbound

This paper cites Focal loss for dense object detection.

Efficient Deep Neural Networks Focal loss for dense object detection

Reference 90

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source=pdf_text observed=2026-08-14T12:19:53.701829Z digest=sha256:5dbc7cf482c693b7d19329d8a3cfa9c24017c1ce9a13224a49fbc14c69fd1d80

Observation adbf9402-281a-47b9-8375-28b22d6c4407 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Efficient Deep Neural Networks Microsoft COCO: Common Objects in Context

Reference 91

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source=pdf_text observed=2026-08-14T12:19:53.707865Z digest=sha256:b29f46b8754309d65840935aa664fb923755f7726573057615dd206b03390d95

Observation b611cfb5-8452-4258-9f05-9c3e9835586d · outbound

This paper cites Progressive Neural Architecture Search.

Efficient Deep Neural Networks Progressive Neural Architecture Search

Reference 92

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source=pdf_text observed=2026-08-14T12:19:53.721030Z digest=sha256:1ca03e8f4496abbcd42af09ce36c92eea0b04ea5a434efea2987b282268c1111

Observation 1bc00182-e4f2-4565-bd0c-e91e64da5ebb · outbound

This paper cites DARTS: Differentiable Architecture Search.

Efficient Deep Neural Networks DARTS: Differentiable Architecture Search

Reference 93

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source=pdf_text observed=2026-08-14T12:19:53.727731Z digest=sha256:e8d4a8fdb2953eb7cfff8b51df8b24e8719ea609056671f7429e793b088c3a60

Observation 688992c3-8f30-4fe5-af6e-c2bc26876f3d · outbound

This paper cites Coupled generative adversarial networks.

Efficient Deep Neural Networks Coupled generative adversarial networks

Reference 94

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source=pdf_text observed=2026-08-14T12:19:53.734064Z digest=sha256:eace3d44e0af3bcbccf13744f5091e8668bcb5a98c4ad166f7b935660eb75171

Observation 8646009a-4b4a-41cd-b549-0b1ee5106c5d · outbound

This paper cites Ssd: Single shot multibox detector.

Efficient Deep Neural Networks Ssd: Single shot multibox detector

Reference 95

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source=pdf_text observed=2026-08-14T12:19:53.741982Z digest=sha256:25643242294ca17ac4bcfc0b9a5ebe8c2a0f24fc54e6fc84279ab5110f857d84

Observation 9d35c388-f72b-4f4a-a71d-f832f2b56a6d · outbound

This paper cites MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning.

Efficient Deep Neural Networks MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

Reference 96

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Observation 06c4b7cb-555e-4bdf-87b9-1219ad9058bb · outbound

This paper cites Fully Convolutional Networks for Semantic Segmentation.

Efficient Deep Neural Networks Fully Convolutional Networks for Semantic Segmentation

Reference 97

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Observation cb0c2b90-04a8-4f0e-b733-44496e412542 · outbound

This paper cites Learning transferable features with deep adaptation net- works.

Efficient Deep Neural Networks Learning transferable features with deep adaptation net- works

Reference 98

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source=pdf_text observed=2026-08-14T12:19:53.766341Z digest=sha256:358ba49a26c36d8243e3b6b1c93a7c6a49f20f66edfe7ed63bac07b84b11107e

Observation b22a9383-bee0-4baf-8463-67b09d4452c9 · outbound

This paper cites ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.

Efficient Deep Neural Networks ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Reference 99

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source=pdf_text observed=2026-08-14T12:19:53.772804Z digest=sha256:32d557e401d3bb057aa1148a062887f1162c87304ecac7cf62993ceb2e859e7d

Observation b6cebff1-c011-49d4-82dd-e5d4dc448534 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Efficient Deep Neural Networks The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 100

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source=pdf_text observed=2026-08-14T12:19:53.778296Z digest=sha256:0821588e57c548ba66dec386c1297be22d04c383026fce4d5bdf1ebf9e30a457

Observation 2367b1b8-4224-4def-b431-07b8bade5d47 · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.

Efficient Deep Neural Networks TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems

Reference 101

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Observation 9657d810-31b2-4cb0-a563-a18cf7329c0d · outbound

This paper cites 3d convolutional neural networks for land- ing zone detection from lidar.

Efficient Deep Neural Networks 3d convolutional neural networks for land- ing zone detection from lidar

Reference 102

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Pith citing papers

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