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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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5

코드

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

https://github.com/jiupinjia/stylized-neural-painting

Stylized Neural Painting

https://github.com/jkkummerfeld/emnlp20lm

Regularizing and Optimizing LSTM Language Models

https://github.com/jlibovicky/char-nmt-fairseq

Neural Machine Translation without Embeddings

https://github.com/jlrussin/syntactic_attention

Compositional generalization in a deep seq2seq model by separating syntax and semantics

https://github.com/jm12138/Paddle-CheXNet

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/jmcrego/bisync

Bilingual Synchronization: Restoring Translational Relationships with Editing Operations

https://github.com/jmnolte/hccnet

Attention Is All You Need

https://github.com/jmyrberg/finnlem

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

https://github.com/joebluems/Soul_Of_AI

Attention Is All You Need

https://github.com/john-bradshaw/rxn-lm

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

https://github.com/johngull/gradinit

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training

https://github.com/johnr0/TaleBrush-backend

Neural Machine Translation of Rare Words with Subword Units

https://github.com/johnrobinsn/catch

Recurrent Models of Visual Attention

https://github.com/johnthebrave/nlidb-datasets

Dataset for a Neural Natural Language Interface for Databases (NNLIDB)

https://github.com/jomimc/FoldAsymCode

Structural asymmetry along protein sequences and co-translational folding

https://github.com/jonas-becker/text-generation

BERTScore: Evaluating Text Generation with BERT

https://github.com/jonas-koehler/s2cnn

Convolutional Networks for Spherical Signals

https://github.com/jongcye/CollaGAN_MRI

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

https://github.com/jongwooko/nash-pruning-official

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

https://github.com/jonkhler/s2cnn

Convolutional Networks for Spherical Signals

https://github.com/jonryf/deep-learning-image-colorization-using-gan

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/jonvthvn90/GitHbProject

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/joongbo/tta

Attention Is All You Need

https://github.com/josephch405/curriculum-nmt

Improving Neural Machine Translation Models with Monolingual Data

https://github.com/josh-ashkinaze/Normalized-Google-Distance

The Google Similarity Distance

https://github.com/jowitapodolak/finetuning_BERT_NER

How multilingual is Multilingual BERT?

https://github.com/joytianya/google_bert

Attention Is All You Need

https://github.com/jpark621/language-style-transfer

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/json9512/sonarToimage

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/jsuarez5341/Efficient-Dynamic-Batching

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

https://github.com/jtiger958/cyclegan-pytorch

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/juletx/image-caption-generation

Show and Tell: A Neural Image Caption Generator

https://github.com/juliakreutzer/bandit-neuralmonkey

Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

https://github.com/jun0wanan/stnbhwd

Spatial Transformer Networks

https://github.com/jungokasai/XOR_QA_MTPipeline

XOR QA: Cross-lingual Open-Retrieval Question Answering

https://github.com/jungokasai/billboard

Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation

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