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AdaSpring
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/0470018860.s00096openalex-snapshot
Catastrophic Forgetting in Connectionist Networks
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_48openalex-snapshot
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
inferredcites →https://doi.org/10.1016/j.jcp.2018.04.018openalex-snapshot
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
inferredcites →https://doi.org/10.1016/j.patcog.2020.107461openalex-snapshot
AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference
inferredcites →https://doi.org/10.1016/s1364-6613(99)01294-2openalex-snapshot
Catastrophic forgetting in connectionist networks
inferredcites →https://doi.org/10.1073/pnas.1611835114openalex-snapshot
Overcoming catastrophic forgetting in neural networks
inferredcites →https://doi.org/10.1109/comst.2020.2970550openalex-snapshot
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.643openalex-snapshot
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning
inferredcites →https://doi.org/10.1109/cvpr.2018.00257openalex-snapshot
Practical Block-Wise Neural Network Architecture Generation
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2018.00907openalex-snapshot
Learning Transferable Architectures for Scalable Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00919openalex-snapshot
BlockDrop: Dynamic Inference Paths in Residual Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00293openalex-snapshot
MnasNet: Platform-Aware Neural Architecture Search for Mobile
inferredcites →https://doi.org/10.1109/cvpr.2019.01166openalex-snapshot
ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation
inferredcites →https://doi.org/10.1109/cvprw.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvprw.2018.00215openalex-snapshot
SqueezeNext: Hardware-Aware Neural Network Design
inferredcites →https://doi.org/10.1109/iccv.2017.155openalex-snapshot
Channel Pruning for Accelerating Very Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2019.00138openalex-snapshot
Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation
inferredcites →https://doi.org/10.1109/iccv.2019.00190openalex-snapshot
AutoDispNet: Improving Disparity Estimation With AutoML
inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-snapshot
BranchyNet: Fast inference via early exiting from deep neural networks
inferredcites →https://doi.org/10.1109/ipsn.2016.7460664openalex-snapshot
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
inferredcites →https://doi.org/10.1109/tc.2020.3015531openalex-snapshot
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
inferredcites →https://doi.org/10.1109/tcad.2018.2858384openalex-snapshot
DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters
inferredcites →https://doi.org/10.1109/tmc.2020.2999956openalex-snapshot
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles
inferredcites →https://doi.org/10.1109/tnnls.2022.3199703openalex-snapshot
A Progressive Subnetwork Searching Framework for Dynamic Inference
inferredcites →https://doi.org/10.1109/vlsid.2019.00056openalex-snapshot
The Ramifications of Making Deep Neural Networks Compact
inferredcites →https://doi.org/10.1145/2623330.2623612openalex-snapshot
Efficient mini-batch training for stochastic optimization
inferredcites →https://doi.org/10.1145/2939672.2939839openalex-snapshot
Compressing Convolutional Neural Networks in the Frequency Domain
inferredcites →https://doi.org/10.1145/2994551.2994564openalex-snapshot
Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3210240.3210337openalex-snapshot
On-Demand Deep Model Compression for Mobile Devices
inferredcites →https://doi.org/10.1145/3397332openalex-snapshot
Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices
inferredcites →https://doi.org/10.1609/aaai.v32i1.11709openalex-snapshot
Efficient Architecture Search by Network Transformation
inferredcites →https://doi.org/10.18653/v1/d19-1367openalex-snapshot
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
inferredcites →https://doi.org/10.24963/ijcai.2019/480openalex-snapshot
Play and Prune: Adaptive Filter Pruning for Deep Model Compression
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1706.01215openalex-snapshot
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
inferredcites →https://doi.org/10.48550/arxiv.1710.09282openalex-snapshot
A Survey of Model Compression and Acceleration for Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1711.00436openalex-snapshot
Hierarchical Representations for Efficient Architecture Search
inferredcites →https://doi.org/10.48550/arxiv.1806.09228openalex-snapshot
Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
inferredcites →https://doi.org/10.48550/arxiv.1810.05331openalex-snapshot
Dynamic Channel Pruning: Feature Boosting and Suppression
inferredcites →https://doi.org/10.48550/arxiv.1903.11728openalex-snapshot
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
inferredcites →https://doi.org/10.48550/arxiv.1907.11357openalex-snapshot
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1908.09791openalex-snapshot
Once-for-All: Train One Network and Specialize it for Efficient Deployment
inferredcites →https://doi.org/10.48550/arxiv.2006.02903openalex-snapshot
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
inferredcites →https://doi.org/10.48550/arxiv.2006.05675openalex-snapshot
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition
inferredcites →https://doi.org/10.48550/arxiv.2006.16537openalex-snapshot
Theory-Inspired Path-Regularized Differential Network Architecture Search
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10844-025-00919-7openalex-snapshot
Integrating control-theoretic predictive deep learning for resource-efficient computing systems
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.02.006openalex-snapshot
Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead
inferred← citeshttps://doi.org/10.1109/compsac61105.2024.00123openalex-snapshot
A Learning-Based and Network-Aware Power Management for Mobile Devices
inferred← citeshttps://doi.org/10.1109/comst.2023.3319952openalex-snapshot
Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey
inferred← citeshttps://doi.org/10.1109/edge55608.2022.00034openalex-snapshot
A Novel Heterogeneous Computing Middleware for Mobile AI Services
inferred← citeshttps://doi.org/10.1109/jiot.2022.3176136openalex-snapshot
CAQ: Toward Context-Aware and Self-Adaptive Deep Model Computation for AIoT Applications
inferred← citeshttps://doi.org/10.1109/jiot.2022.3194726openalex-snapshot
CrowdHMT: Crowd Intelligence With the Deep Fusion of Human, Machine, and IoT
inferred← citeshttps://doi.org/10.1109/jiot.2023.3279579openalex-snapshot
Content-Aware Adaptive Device–Cloud Collaborative Inference for Object Detection
inferred← citeshttps://doi.org/10.1109/jiot.2024.3410015openalex-snapshot
Research on Multifeature Fusion False Review Detection Based on DynDistilBERT-BiLSTM-CNN
inferred← citeshttps://doi.org/10.1109/jiot.2024.3414496openalex-snapshot
Sparse Mobile Crowdsensing for Gas Monitoring in Coal Mine Working Face
inferred← citeshttps://doi.org/10.1109/swc57546.2023.10449264openalex-snapshot
EnergySense: A Fine-Grained Energy Analysis Framework for DNN Processing with Low-Power Ubiquitous Sensors
inferred← citeshttps://doi.org/10.1109/tmc.2023.3316388openalex-snapshot
AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices
inferred← citeshttps://doi.org/10.1109/tmc.2023.3335853openalex-snapshot
Greta: Towards a General Roadside Unit Deployment Framework
inferred← citeshttps://doi.org/10.1109/tmc.2024.3355764openalex-snapshot
SwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory Budget
inferred← citeshttps://doi.org/10.1109/tmc.2024.3487294openalex-snapshot
ClassTer: Mobile Shift-Robust Personalized Federated Learning via Class-Wise Clustering
inferred← citeshttps://doi.org/10.1109/tmc.2025.3549399openalex-snapshot
CrowdHMTware: A Cross-Level Co-Adaptation Middleware for Context-Aware Mobile DL Deployment
inferred← citeshttps://doi.org/10.1109/tmc.2025.3572215openalex-snapshot
AdaShift: Anti-Collapse and Real-Time Deep Model Evolution for Mobile Vision Applications
inferred← citeshttps://doi.org/10.1145/3570361.3592524openalex-snapshot
A Workload-Aware DVFS Robust to Concurrent Tasks for Mobile Devices
inferred← citeshttps://doi.org/10.1145/3613904.3642109openalex-snapshot
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
inferred← citeshttps://doi.org/10.1145/3630098openalex-snapshot
AdaMEC: Towards a Context-adaptive and Dynamically Combinable DNN Deployment Framework for Mobile Edge Computing
inferred← citeshttps://doi.org/10.1145/3643832.3661878openalex-snapshot
Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs
inferred← citeshttps://doi.org/10.1145/3665868openalex-snapshot
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
inferred← citeshttps://doi.org/10.1145/3666025.3699327openalex-snapshot
PieBridge: Fast and Parameter-Efficient On-Device Training via Proxy Networks
inferred← citeshttps://doi.org/10.1145/3666025.3699339openalex-snapshot
AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments
inferred← citeshttps://doi.org/10.1145/3698383.3699621openalex-snapshot
Spatio-Temporal Synergy with ViT: Enhancing Collaborative Perception and Object Detection for Heterogeneous Agents
inferred← citeshttps://doi.org/10.1145/3699771openalex-snapshot
UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices
inferred← citeshttps://doi.org/10.1145/3798107openalex-snapshot
LAG-Guided Runtime Framework: Block-Level Scheduling and Dynamic Compression for Multi-DNN Environments
inferred← citeshttps://doi.org/10.3390/iot7010006openalex-snapshot
A Systematic Review of Self-Adaptive Mobile Applications with Cooperative Dimension
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