Abstract: Class incremental learning (CIL) aims to enable models to continuously learn new classes without catastrophically forgetting old ones. A promising direction is to learn and use prototypes of ...
CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...
Abstract: Our study focuses on detecting confusion from EEG data in higher education. Using BiLSTM models, we enhance EEG analysis efficiency and precision. Starting with extensive feature extraction ...
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