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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mits58/Pointer-Networks

Sequence to Sequence Learning with Neural Networks

https://github.com/mjhucla/TF-mRNN

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

https://github.com/mkunes/w2v2_audioframeclassification

Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0

https://github.com/mlennox/summarisers

Sequence to Sequence Learning with Neural Networks

https://github.com/mmcux/de-nds-translation

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

https://github.com/modelscope/modelscope

Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation

https://github.com/moejoe95/crf-vs-rnn-ner

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/monologg/JointBERT

BERT for Joint Intent Classification and Slot Filling

https://github.com/monologg/R-BERT

Enriching Pre-trained Language Model with Entity Information for Relation Classification

https://github.com/moon23k/Attention_Anchors

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/moon23k/LSTM_Anchors

Sequence to Sequence Learning with Neural Networks

https://github.com/moon23k/RNN_Seq2Seq

Sequence to Sequence Learning with Neural Networks

https://github.com/mp2893/gram

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/mpavlovic/insincere-questions-classifier

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/mpc001/Visual_Speech_Recognition_for_Multiple_Languages

End-to-end Audio-visual Speech Recognition with Conformers

https://github.com/mpc001/auto_avsr

End-to-end Audio-visual Speech Recognition with Conformers

https://github.com/mrcmoresi/sc-lstm

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

https://github.com/mrdbourke/tensorflow-deep-learning

PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

https://github.com/mrsalehi/ground-sentence-video

TALL: Temporal Activity Localization via Language Query

https://github.com/ms8909/LipONet

LipNet: End-to-End Sentence-level Lipreading

https://github.com/mszpc/SoftMaskedBert

Spelling Error Correction with Soft-Masked BERT

https://github.com/mtanti/coco-caption

CIDEr: Consensus-based Image Description Evaluation

https://github.com/mtbarta/hyperbolic

Hyperbolic Neural Networks

https://github.com/mtzcorporations/nlp_teamjodka

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/munir-bd/Korean-POS-Tagger-LSTM

Sequence to Sequence Learning with Neural Networks

https://github.com/mwbrulhardt/yelp-absa

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

https://github.com/myaooo/RNNVis

Understanding Hidden Memories of Recurrent Neural Networks

https://github.com/mzhaoshuai/slimclr

Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training

https://github.com/n-waves/multifit

A Corpus for Multilingual Document Classification in Eight Languages

https://github.com/nachotp/BertCommentSum

Text Summarization with Pretrained Encoders

https://github.com/naiwo/aste-transformer

ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet Extraction

https://github.com/najafmurtaza/Developing-Machine-Learning-Models-in-Flask

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

https://github.com/nakhunchumpolsathien/TR-TPBS

Text Summarization with Pretrained Encoders

https://github.com/nakhunchumpolsathien/ThaiSum

AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

https://github.com/nalbert9/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/namanphy/style-transfer-through-back-translate

Style Transfer Through Back-Translation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.