{"total":23,"rows":[{"id":"2507.10800","title":"ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference","created":"2025-07-14","cited_by_count":7,"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":"2506.03938","title":"FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review","created":"2025-06-04","cited_by_count":2,"area":null},{"id":"2510.07782","title":"RCPU: Rotation-Constrained Error Compensation for Structured Pruning of Large Language Models","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2601.07795","title":"Vision-Language Model for Accurate Crater Detection","created":"2026-01-12","cited_by_count":2,"area":null},{"id":"2506.11093","title":"EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2509.10334","title":"I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2507.21364","title":"Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers","created":"2025-07-28","cited_by_count":0,"area":null},{"id":"2509.15436","title":"Region-Aware Deformable Convolutions","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2512.08198","title":"Animal Re-Identification on Microcontrollers","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2512.11260","title":"Do We Need Reformer for Vision? An Experimental Comparison with Vision Transformers","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2601.03290","title":"Lightweight Transformer Architectures for Edge Devices in Real-Time Applications","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2603.10538","title":"DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2603.18585","title":"HAViT: Historical Attention Vision Transformer","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2604.08435","title":"HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2605.01742","title":"Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2606.09960","title":"HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2607.10942","title":"Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2607.27251","title":"Recursive transformers for semiconductor thermo-mechanical reliability","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2607.28589","title":"MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2609.01212","title":"Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.25515","title":"Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.31159","title":"Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence","created":"2026-09-25","cited_by_count":0,"area":null}],"next_cursor":null}