ترجمه فارسی مقاله کشف اثرات طولانی مدت بر تنظیم دقیق پارامتر

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
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• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
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• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
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• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
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• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

🎯 این بسته یک دورهٔ آموزشی کامل و چندلایه است؛ شامل ویدیوهای آموزشی، کتاب‌ها، تمرین‌ها و خودآزمایی.

چکیده

Pre-trained Artificial Neural Networks (ANNs) exhibit robust pattern recognition capabilities and share extensive similarities with the human brain, specifically Biological Neural Networks (BNNs). We are particularly intrigued by these models' ability to acquire new knowledge through fine-tuning. In this regard, Parameter-efficient Fine-tuning (PEFT) has gained widespread adoption as a substitute for full fine-tuning due to its cost reduction in training and mitigation of over-fitting risks by limiting the number of trainable parameters during adaptation. Since both ANNs and BNNs propagate information layer-by-layer, a common analogy can be drawn: weights in ANNs represent synapses in BNNs, while features (also known as latent variables or logits) in ANNs represent neurotransmitters released by neurons in BNNs. Mainstream PEFT methods aim to adjust feature or parameter values using only a limited number of trainable parameters (usually less than 1% of the total parameters), yet achieve surprisingly good results. Building upon this clue, we delve deeper into exploring the connections between feature adjustment and parameter adjustment, resulting in our proposed method Synapses & Neurons (SAN) that learns scaling matrices for features and propagates their effects towards posterior weight matrices. Our approach draws strong inspiration from well-known neuroscience phenomena - Long-term Potentiation (LTP) and Long-term Depression (LTD), which also reveal the relationship between synapse development and neurotransmitter release levels. We conducted extensive comparisons of PEFT on 26 datasets using attention-based networks as well as convolution-based networks, leading to significant improvements compared to other tuning methods (+8.5% over fully-finetune, +7% over Visual Prompt Tuning, and +3.2% over LoRA). The codes would be released.

چکیده به فارسی (ترجمه ماشینی)