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Paper Citation Record · LEDGER

Efficient Deep Neural Networks

As of 15 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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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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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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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

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

Reference 20

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

Reference 21

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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

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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

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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

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Efficient Deep Neural Networks Fast R-CNN

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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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source=pdf_text observed=2026-08-14T12:19:53.418650Z digest=sha256:3eb5ff78c9688368bfe6bd41eefd1d0af05207217cf33147dd92546607bfe940

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

Reference 42

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source=pdf_text observed=2026-08-14T12:19:53.425414Z digest=sha256:47db778ded06af5d35b1c1e0d866ead043ac52c93f13b8e47461f2076ac4bbed

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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source=pdf_text observed=2026-08-14T12:19:53.431950Z digest=sha256:b7d19aa0195291b25f87f3e117205bf2f7b1d78211a8e77bab4210724e58d408

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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source=pdf_text observed=2026-08-14T12:19:53.437827Z digest=sha256:9e7a9fafd393041ba9bd1ecbb2d4f119ae84a24893f21ef4185dbf72b6a5cae5

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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source=pdf_text observed=2026-08-14T12:19:53.443903Z digest=sha256:d0789e4237c12b8d8890d79e0d307cd756a0954a4d3ab0a5a3a9767fbbfab01b

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=sha256:424fc403c7c2641b94418a971a4241e057b1a96d64b5f9447e572d52d5767c18

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=sha256:263d4acdb59dbb1078cbdc04c15bd53db006cf4e2c5a5ecd2111125d18ce6c8a

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=sha256:9d97859fd63387c4f4cdad3e731810053c659a0112b0072df1a370fcfe47f3ed

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:a7beea1554145d2ae6f58d1f5cc886cdc745802eff4f9ed9810f461e77644f26

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:b14cbddceb34764571f9c1e777e13319ecbd6415e92d91de29adbcf8fe2e3c6c

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:d4c4cfdce462073ad894161d5b57fe8d3a1a6d6d1bd1304d6101edce78f84eb2

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=sha256:0932855c2503a490897733bb54b061d50c970fd9fd0ea8779b4893d26c831be4

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=sha256:1ac7d55a55be1e6526728c625cb71a3e0496f62ab71639fc90a0edb3b05a02e1

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=sha256:3417d43f0b5543c4cdf4f263dd31f171a0c7a2dbc916f7385e75200d033b0678

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=sha256:41c6fab42b99a7bbd9e20224fcdef441e329538cea3cfb92f04ebd96c2902e43

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

Reference 58

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source=pdf_text observed=2026-08-14T12:19:53.516223Z digest=sha256:d11ed7c5f334688fe07a44de270a50368ddf79cdb3c53056fe76825861d6af0c

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:e9f3553791796114ad97aaba2cd80d7c03cf257a66bd4d38052c184c05865301

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

This paper cites Squeeze-and-excitation networks.

Efficient Deep Neural Networks Squeeze-and-excitation networks

Reference 60

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source=pdf_text observed=2026-08-14T12:19:53.534338Z digest=sha256:f0b53e73841c9afe49ac9fc51a38d1fd7484530f74a77d95a53d6f02422360ee

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=sha256:2c9b09ae99397efd70305e5e906f011f2df107d0bb1c5dbcb9e281a50be4e5b7

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

Reference 62

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source=pdf_text observed=2026-08-14T12:19:53.544490Z digest=sha256:5877886f228473132b2fa4fcc7319de000269169222b7046a8fb02186e06e683

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

Reference 63

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source=pdf_text observed=2026-08-14T12:19:53.550345Z digest=sha256:e33bac12b40a57c6d19948ca681c7bd48738775e6ffb0d069e73bbe5ac59364b

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

Reference 64

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source=pdf_text observed=2026-08-14T12:19:53.555866Z digest=sha256:2a8453f8f076d975bf65cc4df98a07009cd3a1f64ff3887aa69acb08eb1d7fed

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=sha256:57546e34a20d5f1543f3de10d6239c1a38154dbd33b237fd255d9b6769f0f118

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

Reference 67

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source=pdf_text observed=2026-08-14T12:19:53.571543Z digest=sha256:2b72846b555f6d467e1463c344bec7ea597da65f4a734a9ffa77e1faca258b7a

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

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Efficient Deep Neural Networks Categorical Reparameterization with Gumbel-Softmax

Reference 68

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source=pdf_text observed=2026-08-14T12:19:53.576669Z digest=sha256:9d1e97c7cd1c9a65bb0c872c0baf537ee5f9e41a6a5f42d45579cfa2ab8cd128

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

Reference 69

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source=pdf_text observed=2026-08-14T12:19:53.581656Z digest=sha256:66f8cd982a456ddb300180db0dc90e564a7f4281f1ac28f9e28ad6aea4734368

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:a7b6288cd04da21878915565bdd19b94257657ad3449edc2b7caf562ad6c0b74

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

Reference 71

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source=pdf_text observed=2026-08-14T12:19:53.591332Z digest=sha256:d1dae44e40346f1c76d39dbd2055368ede967a4cf5d14f247d245b12c550643b

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?

Reference 72

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source=pdf_text observed=2026-08-14T12:19:53.596401Z digest=sha256:153f9df88f486fdc99c3253e2eb5b0c6d713978a6d59ed0ce659436808473163

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?

Reference 73

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source=pdf_text observed=2026-08-14T12:19:53.602134Z digest=sha256:5ad7abf0fad41fcaece520fa01b289183f1a9b3da4b2230da16039e3899aa632

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

This paper cites Local Binary Convolutional Neural Networks.

Efficient Deep Neural Networks Local Binary Convolutional Neural Networks

Reference 74

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source=pdf_text observed=2026-08-14T12:19:53.606975Z digest=sha256:f703979908d7b16fedfe63341ba897e7970ed08d59026cab18fa3884327a83df

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=sha256:51286b7f44e542d38d24d7f76604d876e91ed7286c3eaa502b4951b3adcc6768

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

Reference 76

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source=pdf_text observed=2026-08-14T12:19:53.616787Z digest=sha256:98f48d5a92791bacb8dd2469eed25f3e2bcc0d2b06b467aaa18dbab1d2d9cb5c

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

This paper cites Free supervision from video games.

Efficient Deep Neural Networks Free supervision from video games

Reference 77

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source=pdf_text observed=2026-08-14T12:19:53.621277Z digest=sha256:9daa48a7fc9d0d1db0591491bcc2cd18af701b1d6ccee337cf238cb42a375013

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

Reference 78

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source=pdf_text observed=2026-08-14T12:19:53.625762Z digest=sha256:1562874ed6ff872a36f06dadd90042a02f7d131ea113ef451f9d429f6302c7d6

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

Reference 79

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source=pdf_text observed=2026-08-14T12:19:53.632080Z digest=sha256:81b5fdc8ce94663cd5792fc474b4394d77bb8bacb46eaa3cd8877c5aac9cccd7

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:d3c5db34fec963401e95f2fadd92606deb5240a7de9364d50462bc5b7451f627

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:e91834453ee96ae3337dcd36e6f69ea556b5a0470e1052d4adf7b4db4552e7b9

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

Reference 82

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source=pdf_text observed=2026-08-14T12:19:53.650101Z digest=sha256:8f14badd08d48e6feeec94831573c99cb0eecaa33165afbc3a79227756c43608

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

Reference 83

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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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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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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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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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no resolver link, observed 2026-08-14T12:19:53.688853Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:19:53.688853Z digest=sha256:4bb930a0b9cf24804a20669fd9f0196668191e5481f2f222d2cdeb85bca8b04a

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=sha256:47bbbc49c71beeb952fe0563ff1a26133703b37b957b4a4628261ca6f484b15b

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:19:53.701829Z digest=sha256:c7b84e2cf23abaee6d3ed7bb6aced461454f2dca68a3ebd6e75a7f5035623d13

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:eb4a251e47463cf20568712050f64028772c3a42bb3132a4ba4e61a5e81deb65

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:d4e0819f22ca299f74376eb2817de5d4dc5d5376961da02b782c0d10c778e05c

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:19:53.727731Z digest=sha256:93649851950081f9726fc16679208636e1d3540ebb999541afe53d6abeb01cc1

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:19:53.734064Z digest=sha256:de262fd1fb0e43f4a969a61773c88648eb168dc2fdae368224b000c77ca77790

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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no resolver link, observed 2026-08-14T12:19:53.741982Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:19:53.741982Z digest=sha256:2f70d6011d0c5214573e7feb7a34792c947eb9ae2aec5a0894eae0bc29b67fa4

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-14T12:19:53.748263Z digest=sha256:c5808dadaaa758fcd75e3386ece181deb8369060b2ebeaebbbe17cce5005a8f3

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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source=pdf_text observed=2026-08-14T12:19:53.753905Z digest=sha256:ac9588c8324feb4c512d87af1aac47c66c777947ef7428e8e7d6bc624abddc7c

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:e2a72bc281ecda02a6ea25e8bddc1da09b2287db64ddb220398d441f86204e15

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:c5bf73d056330b12ab58205a3f87c265ce8a75a3c4f6809f9e2f898453627cd6

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:ba3c6a4589e82fdd393fafe187455deeac1adb67e1e80aca0f6f4a7f85a394ea

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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source=pdf_text observed=2026-08-14T12:19:53.784158Z digest=sha256:8095e7a670e7de0081194cc017b347d1c9716acac908befea1c57cd35ee1595d

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

No inbound Pith citation observations are available.