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코드

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

https://github.com/dalek7/attention

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/dalmia/Quora-Question-Pairs

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/dan11003/CFEAR_Radarodometry_code_public

CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry

https://github.com/danenergetics/returnn

RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition

https://github.com/danielajisafe/Image-Captioning-Model

Show and Tell: A Neural Image Caption Generator

https://github.com/danieldeutsch/bert_score_content_analysis

BERTScore: Evaluating Text Generation with BERT

https://github.com/danielhanchen/hyperlearn

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach

https://github.com/danielinux7/Multilingual-Parallel-Corpus

On the Impact of Various Types of Noise on Neural Machine Translation

https://github.com/danielplatt/convolutions

Spectral Networks and Locally Connected Networks on Graphs

https://github.com/dantecomedia/CycleGAN

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

https://github.com/daphne12345/SummarizationRadiologyReports

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/darr/spatial_transformer_networks

Spatial Transformer Networks

https://github.com/darth-c0d3r/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/datalogue/keras-attention

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/dathudeptrai/TensorflowTTS

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/davidalbertonogueira/NLP-tutorials

Attention Is All You Need

https://github.com/davidmrau/mixture-of-experts

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

https://github.com/davidsvaughn/dts-tf

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/davinhill/Convolution_Captioning

Convolutional Image Captioning

https://github.com/davisking/dlib-models

Attention Is All You Need

https://github.com/dawn0815/UniSA

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

https://github.com/dbash/CycleGAN3D

Unsupervised Video-to-Video Translation

https://github.com/dccuchile/CC6204

Attention Is All You Need

https://github.com/deciding/ParallelWaveGAN

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/dedhiaparth98/spatial-transformer-network

Spatial Transformer Networks

https://github.com/deep-spin/OpenNMT-entmax

OpenNMT: Neural Machine Translation Toolkit

https://github.com/deep-spin/quati

Sparse and Continuous Attention Mechanisms

https://github.com/deep-spin/sigmorphon-seq2seq

Self-Regulated Interactive Sequence-to-Sequence Learning

https://github.com/deepmind/haiku

Attention Is All You Need

https://github.com/deepmipt/bert/tree/feat/multi_gpu

Attention Is All You Need

https://github.com/deepqmc/deepqmc

Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks

https://github.com/deepsense-ai/unblackboxing_webinar

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/deepsing-ai/deepsing

deepsing: Generating Sentiment-aware Visual Stories using Cross-modal Music Translation

https://github.com/deepware/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/dellixx/TransERR

TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation

https://github.com/delta6189/AttentionGAN

Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

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