{"total":33,"rows":[{"id":"2506.18927","title":"From Tiny Machine Learning to Tiny Deep Learning: A Survey","created":"2025-06-21","cited_by_count":5,"area":null},{"id":"2505.17662","title":"Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2603.08725","title":"Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review","created":"2026-02-18","cited_by_count":4,"area":null},{"id":"2505.18695","title":"AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification","created":"2025-05-24","cited_by_count":3,"area":null},{"id":"2602.13496","title":"Future of Edge AI in biodiversity monitoring","created":"2026-02-13","cited_by_count":3,"area":null},{"id":"2505.11417","title":"EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactions","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2510.01439","title":"Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons","created":"2025-10-01","cited_by_count":2,"area":null},{"id":"2503.20786","title":"Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark","created":"2025-03-26","cited_by_count":1,"area":null},{"id":"2506.18501","title":"Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2601.00543","title":"ECR: Manifold-Guided Semantic Cues for Compact Language Models","created":"2026-01-02","cited_by_count":1,"area":null},{"id":"2602.13479","title":"GLIMPSE : Real-Time Text Recognition and Contextual Understanding for VQA in Wearables","created":"2026-02-13","cited_by_count":1,"area":null},{"id":"2603.15900","title":"The Internet of Physical AI Agents: Interoperability, Longevity, and the Cost of Getting It Wrong","created":"2026-03-16","cited_by_count":1,"area":null},{"id":"2605.27435","title":"When NPUs Are Not Always Faster: A Stage-Level Analysis of Mobile LLM Inference","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2507.08151","title":"Distilling Empathy from Large Language Models","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2508.04976","title":"CSRAP: Enhanced Canvas Attention Scheduling for Real-Time Mission Critical Perception","created":"2025-08-07","cited_by_count":0,"area":null},{"id":"2511.00335","title":"Beyond ImageNet: Understanding Cross-Dataset Robustness of Lightweight Vision Models","created":"2025-11-01","cited_by_count":0,"area":null},{"id":"2511.22355","title":"AutoTailor: Automatic and Efficient Adaptive Model Deployment for Diverse Edge Devices","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2512.10990","title":"Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2602.18332","title":"MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation","created":"2026-02-20","cited_by_count":0,"area":null},{"id":"2603.29631","title":"Storing Less, Finding More: How Novelty Filtering Improves Cross-Modal Retrieval on Edge Cameras","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.02356","title":"MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.17530","title":"Real-Time Cellist Postural Evaluation With On-Device Computer Vision","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2606.03927","title":"FFR: Forward-Forward Learning for Regression","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.11357","title":"TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.15355","title":"Sustainable Face Recognition on Low-Power Devices with VQ-VAE Embeddings","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2606.29518","title":"Harvesting AI Computation at the Edge via Generic Approximation","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2607.10495","title":"NanoVSR: Towards Real-Time Video Super-Resolution on Edge Devices","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2607.14622","title":"ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2608.14565","title":"Position: AI Lock-In Is in Progress, and We Must Be Prepared","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2608.15310","title":"FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting","created":"2026-08-15","cited_by_count":0,"area":null},{"id":"2608.15693","title":"Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2609.15625","title":"FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2610.02524","title":"BaCP: Backbone Contrastive Pruning for Preserving Representations in Extremely Sparse Neural Networks","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}