encoder3 [X:AI] BART 논문 리뷰 논문 원본 : https://arxiv.org/abs/1910.13461v1 BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionWe present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tra.. 2025. 2. 11. [X:AI] Transformer 논문 리뷰 논문 원본 : https://arxiv.org/abs/1706.03762 Attention Is All You NeedThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a newarxiv.org Abstract지배적인 시퀀스 변환 모델은 인코더와 디코더를 포함하는 RNN 또는 CNN 신경망을 기반으로 함최고의 성.. 2024. 2. 10. [X:AI] Attention 논문 리뷰 논문 원본 : https://arxiv.org/abs/1409.0473 Neural Machine Translation by Jointly Learning to Align and TranslateNeural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the traarxiv.org Abstract신경 기계 번역 (Neur.. 2024. 2. 4. 이전 1 다음
