{"total":31,"rows":[{"id":"2207.09353","title":"Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications","created":"2022-07-19","cited_by_count":175,"area":null},{"id":"2301.11189","title":"Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models","created":"2023-01-26","cited_by_count":94,"area":null},{"id":"2202.06533","title":"An Introduction to Neural Data Compression","created":"2022-02-14","cited_by_count":79,"area":null},{"id":"2201.02693","title":"BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing","created":"2022-01-07","cited_by_count":28,"area":null},{"id":"2203.08875","title":"SC2 Benchmark: Supervised Compression for Split Computing","created":"2022-03-16","cited_by_count":15,"area":null},{"id":"2402.02140","title":"Generative Visual Compression: A Review","created":"2024-02-03","cited_by_count":13,"area":null},{"id":"2302.10681","title":"FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing","created":"2023-02-21","cited_by_count":12,"area":null},{"id":"2503.01505","title":"Lossy Neural Compression for Geospatial Analytics: A Review","created":"2025-03-03","cited_by_count":10,"area":null},{"id":"2207.12397","title":"C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning","created":"2022-07-25","cited_by_count":8,"area":null},{"id":"2204.07314","title":"Feature Compression for Rate Constrained Object Detection on the Edge","created":"2022-04-15","cited_by_count":7,"area":null},{"id":"2209.11607","title":"I-SPLIT: Deep Network Interpretability for Split Computing","created":"2022-09-23","cited_by_count":7,"area":null},{"id":"2403.16677","title":"FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression","created":"2024-03-25","cited_by_count":6,"area":null},{"id":"2501.01420","title":"A Multi-task Supervised Compression Model for Split Computing","created":"2025-01-02","cited_by_count":6,"area":null},{"id":"2407.11224","title":"Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding","created":"2024-07-15","cited_by_count":4,"area":null},{"id":"2306.12691","title":"Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems","created":"2023-06-22","cited_by_count":3,"area":null},{"id":"2308.05959","title":"Learned Point Cloud Compression for Classification","created":"2023-08-11","cited_by_count":3,"area":null},{"id":"2310.05306","title":"Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints","created":"2023-10-08","cited_by_count":3,"area":null},{"id":"2403.17154","title":"On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance","created":"2024-03-25","cited_by_count":3,"area":null},{"id":"2309.17401","title":"Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing","created":"2023-09-29","cited_by_count":2,"area":null},{"id":"2509.15058","title":"Communication Efficient Split Learning of ViTs with Attention-based Double Compression","created":"2025-09-18","cited_by_count":2,"area":null},{"id":"2602.12866","title":"Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding","created":"2026-02-13","cited_by_count":2,"area":null},{"id":"2403.00942","title":"Resilience of Entropy Model in Distributed Neural Networks","created":"2024-03-01","cited_by_count":1,"area":null},{"id":"2411.00907","title":"On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2412.10265","title":"Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication","created":"2024-12-13","cited_by_count":1,"area":null},{"id":"2511.11608","title":"Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2206.08152","title":"Fault-Tolerant Collaborative Inference through the Edge-PRUNE Framework","created":"2022-06-16","cited_by_count":0,"area":null},{"id":"2509.18481","title":"Codebook-Based Adaptive Feature Compression With Semantic Enhancement for Edge-Cloud Systems","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2511.11664","title":"Range Asymmetric Numeral Systems-Based Lightweight Intermediate Feature Compression for Split Computing of Deep Neural Networks","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2603.05172","title":"Trainable Bitwise Soft Quantization for Input Feature Compression","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2604.26492","title":"Adaptive Transform Coding for Semantic Compression","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2609.37575","title":"On Task Scope and Information Retention in Source Coding","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}