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코드

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

https://github.com/benjaminbeilharz/hierarchical-reinforcement-learning

Sockeye: A Toolkit for Neural Machine Translation

https://github.com/benjamintaiwo/Attention

Attention Is All You Need

https://github.com/benpeloquin7/sapir_image_captioning

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/bentrevett/extreme-summarization-of-source-code

A Convolutional Attention Network for Extreme Summarization of Source Code

https://github.com/bentrevett/pytorch-seq2seq

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/benywon/ComQA

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

https://github.com/bfopengradient/NLP_text_summarization_apps

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/bhaddow/dev-nematus

Using the Output Embedding to Improve Language Models

https://github.com/bharathbhimshetty/Image-Captioning-using-Show-Attend-and-Tell-paper

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/bhavitvyamalik/bert

Attention Is All You Need

https://github.com/big-o/transvec

Exploiting Similarities among Languages for Machine Translation

https://github.com/bihanli/yolactBH

YOLACT: Real-time Instance Segmentation

https://github.com/binhetech/bert-application

Attention Is All You Need

https://github.com/biomedsciai/histocartography

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

https://github.com/bionanoimaging/cellSTORM-Tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/bispl-kaist/tf_CollaGAN_BRATS

Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN

https://github.com/biyoml/End-to-End-Mandarin-ASR

Attention-Based Models for Speech Recognition

https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT

Attention-Based Models for Speech Recognition

https://github.com/bknyaz/bmvc_2019

Image Classification with Hierarchical Multigraph Networks

https://github.com/bkoch4142/attention-is-all-you-need-paper

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/blablabananarama/ukiyoGAN

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

https://github.com/blazejdolicki/multilingual-analysis

XNLI: Evaluating Cross-lingual Sentence Representations

https://github.com/blickwinkel1107/making-the-most-of-context-nmt

Towards Making the Most of Context in Neural Machine Translation

https://github.com/bllfpc/TTokDak

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/bloomsburyai/question-generation

Evaluating Rewards for Question Generation Models

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

Attention Is All You Need

https://github.com/bluestyle97/STGAN-pytorch

STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing

https://github.com/bmy4415/DMLAB-intern

Show and Tell: A Neural Image Caption Generator

https://github.com/bojone/keras_lookahead

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/bojone/labse

Language-agnostic BERT Sentence Embedding

https://github.com/bojone/r-drop

R-Drop: Regularized Dropout for Neural Networks

https://github.com/bond005/yandex-shifts-weather

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/booydar/t5-experiments

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

https://github.com/bplank/teaching-dl4nlp

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/brade31919/SRGAN-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/brainsqueeze/text2vec

Neural Machine Translation by Jointly Learning to Align and Translate

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