This paper introduces UR2M, a novel Uncertainty and Resource-aware event detection framework for microcontrollers (MCUs). The framework aims to overcome the challenge of unreliable predictions caused by distribution shifts between…
Continue readingThe paper introduces ‘Superfiltering’, a method for data filtering in instruction tuning of Large Language Models (LLMs). Instruction tuning improves LLMs but often suffers from low-quality and redundant data. The…
Continue readingThe paper presents a novel algorithm, Federated Learning with Accumulated Regularized Embeddings (FLARE), which addresses challenges in decentralized machine learning, specifically in federated learning (FL). The algorithm uses sparse training…
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