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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/kiyoungkim1/LMkor

Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

https://github.com/klarh/geometric_algebra_attention

Geometric Algebra Attention Networks for Small Point Clouds

https://github.com/kmkwon94/ainize-laser

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/kmkwon94/ainized-ganilla-dev

GANILLA: Generative Adversarial Networks for Image to Illustration Translation

https://github.com/kmzzhang/nbi

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

https://github.com/knc6/cdvae

GemNet: Universal Directional Graph Neural Networks for Molecules

https://github.com/knotgrass/attention/blob/main/attn/attention.py

Attention Is All You Need

https://github.com/kohei0209/espnet

Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data

https://github.com/koles289/udacity-Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/kolloldas/torchnlp

Attention Is All You Need

https://github.com/komeiharada/learn_DL_together

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/kotu931226/classifier_transformer_pytorch

Attention Is All You Need

https://github.com/kovanostra/Message-passing-neural-network

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

https://github.com/kovanostra/graph-to-graph

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

https://github.com/kovanostra/message-passing-nn

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

https://github.com/kpe/params-flow

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/kpot/keras-transformer

Attention Is All You Need

https://github.com/kpu/intgemm

Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU

https://github.com/kracwarlock/action-recognition-visual-attention

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

https://github.com/krantirk/BERT-Pretrained-model

Attention Is All You Need

https://github.com/krantirk/CycleGT

CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training

https://github.com/krasserm/fairseq-image-captioning

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

https://github.com/kriesbeck/masked-language-prediction

Attention Is All You Need

https://github.com/krocki/np-transformer

Attention Is All You Need

https://github.com/krystalan/chatgpt_as_nlg_evaluator

Large Language Models Are State-of-the-Art Evaluators of Translation Quality

https://github.com/krystalan/clidsum

The Cross-lingual Conversation Summarization Challenge

https://github.com/ksulima/Unsupervised-method-to-NPL-Polish-language

Neural Machine Translation of Rare Words with Subword Units

https://github.com/ku21fan/cll-str

How multilingual is Multilingual BERT?

https://github.com/kucukemre96/COMP541

Memory-Efficient Adaptive Optimization

https://github.com/kulkarnikeerti/Image-to-image-translation-using-cGAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/kunheek/style-aware-discriminator

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/ky1994/SpeechRecognition

Attention Is All You Need

https://github.com/kylelo/roofdiffusion

Palette: Image-to-Image Diffusion Models

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

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/kyunghyuncho/rl-pong

Trainable Greedy Decoding for Neural Machine Translation

https://github.com/kzkymur/transition

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

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