https://github.com/alixxxin/multi-lang
Unsupervised Multilingual Alignment using Wasserstein Barycenter
Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/allen108108/Model-Optimizer_Implementation
https://github.com/allenai/allennlp-reading-comprehension
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/allenai/mslr-shared-task
https://github.com/allenai/scientific-claim-generation
https://github.com/alondj/Pytorch-Gpipe
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/alpc91/transda
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
https://github.com/alpha-davidson/sun_cgan
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/alphadl/CCAN
Context-Aware Cross-Attention for Non-Autoregressive Translation
https://github.com/alphadl/lookahead.pytorch
https://github.com/am-sirdaniel/Image-Captioning-Model
https://github.com/aman313/SCAN
https://github.com/amar-laksh/lingua
OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network
https://github.com/amazon-science/amazon-multilingual-counterfactual-dataset
https://github.com/amazon-science/multiatis
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
https://github.com/amazon-science/mxeval
https://github.com/amazon-science/tanl
Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amiannn/espnet
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
https://github.com/amitaydr/Hackaton2018
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/amittiwary42/pix2pix-NNFL
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/amitz25/PCCoder
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/amobiny/Recurrent_Attention_Model
https://github.com/anandharaju/Basic_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/ananyacoder/mee_wmt2021
https://github.com/andersbll/autoencoding_beyond_pixels
Autoencoding beyond pixels using a learned similarity metric
https://github.com/andreamad8/Universal-Transformer-Pytorch
https://github.com/andreapdr/gfun
https://github.com/andreeaiana/xmind
No Language Left Behind: Scaling Human-Centered Machine Translation
https://github.com/andrewowens/multisensory
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/andrewyng/translation-agent
How to Design Translation Prompts for ChatGPT: An Empirical Study
https://github.com/andy840314/QANet-pytorch-
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/anh-nn01/Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/anhinga/JuliaCon2021-poster
https://github.com/anhtu-phan/multi-domain-machine-translation
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
https://github.com/anhtu293/transformer_from_scratch
https://github.com/anirbrhm/ImageCaptioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.