As such, we propose a two-stage fine-tuning framework leveraging LLaMA2 and GPT-4 Knowledge Enhancement for recommendation. In particular, we use GPT-4 Instruction-Following data to tune the LLM in first-stage instruction tuning process, achieving lower training costs and better inference ...
Fine-tuning convolutional neural network based on relaxed Bayesian-optimized support vector machine for random-valued impulse noise removal. J. Electron. Imaging 2023, 32, 013006. [Google Scholar] [CrossRef] Satti, P.; Shrotriya, V.; Garg, B.; Surya Prasath, V. DIBS: Distance-and intensity...