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5

코드

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

https://github.com/pengshuyuan/Bert

Attention Is All You Need

https://github.com/pennfranc/hypnettorch

HyperNetworks

https://github.com/penny9287/sockeye

Attention Is All You Need

https://github.com/pennylaneai/generative-quantum-states

Attention Is All You Need

https://github.com/perceivelab/trf-sg2im

SurfaceNet: Adversarial SVBRDF Estimation from a Single Image

https://github.com/peterholderrieth/steerable_cnps

Convolutional Conditional Neural Processes

https://github.com/petuum/adaptdl

AdaScale SGD: A User-Friendly Algorithm for Distributed Training

https://github.com/pgdelbosque/GAN-MRI

Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT

https://github.com/phanideepgampa/IM2LATEX

Convolutional Sequence to Sequence Learning

https://github.com/phanxuanphucnd/Felix

Felix: Flexible Text Editing Through Tagging and Insertion

https://github.com/phaxiong101/P_xiong

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/philip-brohan/weather2weather

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/philipperemy/keras-attention-mechanism

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/philipperemy/keras-tcn

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/philippwirth/awd-lstm-test

Regularizing and Optimizing LSTM Language Models

https://github.com/philippwirth/treelangrnn

Regularizing and Optimizing LSTM Language Models

https://github.com/phisad/keras-shatt

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

https://github.com/phohenecker/pytorch-transformer

Attention Is All You Need

https://github.com/phquanta/DeepPermInvFp

Permutation-equivariant neural networks applied to dynamics prediction

https://github.com/phueb/unmasked

Masked Language Model Scoring

https://github.com/piekey1994/IOM

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

https://github.com/pierrecolombo/nlg_eval_via_simi_measures

Automatic Text Evaluation through the Lens of Wasserstein Barycenters

https://github.com/pikkaay/efficientnet_gpu

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/pingheng001/Cnn-Bert

Attention Is All You Need

https://github.com/pipiPdesu/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/piquil/rydberggpt

Attention Is All You Need

https://github.com/pittyacg/CurriculumSNGAN

Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)

https://github.com/piyushmakhija5/hinglishnorm

hinglishNorm -- A Corpus of Hindi-English Code Mixed Sentences for Text Normalization

https://github.com/piyushmakhija5/normalizationdataset

hinglishNorm -- A Corpus of Hindi-English Code Mixed Sentences for Text Normalization

https://github.com/pkuyym/evolvingattention

Evolving Attention with Residual Convolutions

https://github.com/pliang279/MFN

Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities

https://github.com/plkmo/Transformer-Eng2French

Attention Is All You Need

https://github.com/pnlbwh/CNN-Diffusion-MRIBrain-Segmentation

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/poethan/LEPOR

LEPOR: An Augmented Machine Translation Evaluation Metric

https://github.com/poethan/cushLEPOR

Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods

https://github.com/poifull10/PervasiveAttentionNet

Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction

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