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https://doi.org/10.1145/3418297
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-11018-5_32openalex-snapshot
Multi-style Generative Network for Real-Time Transfer
inferredcites →https://doi.org/10.1007/978-3-319-10593-2_13openalex-snapshot
Learning a Deep Convolutional Network for Image Super-Resolution
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1109/cvpr.2016.265openalex-snapshot
Image Style Transfer Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.280openalex-snapshot
Fast ConvNets Using Group-Wise Brain Damage
inferredcites →https://doi.org/10.1109/cvprw.2017.149openalex-snapshot
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
inferredcites →https://doi.org/10.1109/iccv.2017.155openalex-snapshot
Channel Pruning for Accelerating Very Deep Neural Networks
inferredcites →https://doi.org/10.1109/isscc.2016.7418007openalex-snapshot
14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
inferredcites →https://doi.org/10.1145/2897824.2925974openalex-snapshot
Let there be color!
inferredcites →https://doi.org/10.1145/3020078.3021745openalex-snapshot
ESE
inferredcites →https://doi.org/10.1145/3314221.3314652openalex-snapshot
Wootz: a compiler-based framework for fast CNN pruning via composability
inferredcites →https://doi.org/10.1145/3373376.3378534openalex-snapshot
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
inferredcites →https://doi.org/10.1609/aaai.v34i04.5954openalex-snapshot
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices
inferredcites →https://doi.org/10.1613/jair.374openalex-snapshot
Identifying Hierarchical Structure in Sequences: A linear-time algorithm
inferredcites →https://doi.org/10.48550/arxiv.1608.03665openalex-snapshot
Learning Structured Sparsity in Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1608.08710openalex-snapshot
Pruning Filters for Efficient ConvNets
inferredcites →https://doi.org/10.48550/arxiv.1612.00694openalex-snapshot
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
inferredcites →https://doi.org/10.48550/arxiv.1705.08922openalex-snapshot
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1802.04799openalex-snapshot
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1808.08718openalex-snapshot
Wide Activation for Efficient and Accurate Image Super-Resolution
inferredcites →https://doi.org/10.5555/1622776.1622780openalex-snapshot
Identifying hierarchical structure in sequences

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-37429-5_6openalex-snapshot
ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision–Language Models
inferred← citeshttps://doi.org/10.1016/j.sysarc.2025.103645openalex-snapshot
Neural Architecture Search and Automatic Code Optimization: Techniques, trends, and challenges
inferred← citeshttps://doi.org/10.1080/09540091.2024.2306962openalex-snapshot
AICOM-MP: an AI-based monkeypox detector for resource-constrained environments
inferred← citeshttps://doi.org/10.1109/asap52443.2021.00040openalex-snapshot
Towards Automatic and Agile AI/ML Accelerator Design with End-to-End Synthesis
inferred← citeshttps://doi.org/10.1109/jiot.2022.3194888openalex-snapshot
Rise of the Automotive Health-Domain Controllers: Empowering Healthcare Services in Intelligent Vehicles
inferred← citeshttps://doi.org/10.1109/jproc.2022.3226481openalex-snapshot
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
inferred← citeshttps://doi.org/10.1109/mc.2021.3093428openalex-snapshot
Rise of the Autonomous Machines
inferred← citeshttps://doi.org/10.1109/micro56248.2022.00044openalex-snapshot
GCD2: A Globally Optimizing Compiler for Mapping DNNs to Mobile DSPs
inferred← citeshttps://doi.org/10.1109/tmc.2024.3513457openalex-snapshot
EdgeLLM: Fast On-Device LLM Inference With Speculative Decoding
inferred← citeshttps://doi.org/10.1145/3533251openalex-snapshot
A Framework for Neural Network Architecture and Compile Co-optimization
inferred← citeshttps://doi.org/10.1145/3597933openalex-snapshot
Mitigating Query-based Neural Network Fingerprinting via Data Augmentation
inferred← citeshttps://doi.org/10.1145/3669940.3707239openalex-snapshot
Fast On-device LLM Inference with NPUs
inferred← citeshttps://doi.org/10.1145/3696410.3714553openalex-snapshot
WeInfer: Unleashing the Power of WebGPU on LLM Inference in Web Browsers
inferred← citeshttps://doi.org/10.1145/3746262.3761975openalex-snapshot
Efficient and Accurate Post-Training Sparsification of Large Language Models with Proximal Operators
inferred← citeshttps://doi.org/10.1561/1000000058openalex-snapshot
Cloud and Edge Computing for Connected and Automated Vehicles
inferred← citeshttps://doi.org/10.3390/electronics14122495openalex-snapshot
Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments
inferred← citeshttps://doi.org/10.34133/icomputing.0040openalex-snapshot
Compiler Technologies in Deep Learning Co-Design: A Survey
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