Unsupervised single-generation confidence calibration for reasoning LLMs via offline self-consistency proxy distillation outperforms baselines on math and QA tasks and improves selective prediction.
Empowering edge intelligence: A comprehensive survey on on-device ai models
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Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.
Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
Cello Evaluator is a real-time postural feedback system for cellists running on current Android phones via on-device computer vision, validated as user-friendly by experts.
citing papers explorer
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Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation
Unsupervised single-generation confidence calibration for reasoning LLMs via offline self-consistency proxy distillation outperforms baselines on math and QA tasks and improves selective prediction.
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Position Paper: From Edge AI to Adaptive Edge AI
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.
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A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits
Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
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Real-Time Cellist Postural Evaluation With On-Device Computer Vision
Cello Evaluator is a real-time postural feedback system for cellists running on current Android phones via on-device computer vision, validated as user-friendly by experts.