Papers with Code 아카이브

NLP based Person Retrival

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코드

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

https://github.com/neulab/knn-transformers

Nearest Neighbor Machine Translation

https://github.com/neuropoly/multiclass-segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/nfelnlp/nfelnlp

Inseq: An Interpretability Toolkit for Sequence Generation Models

https://github.com/ngocphucck/BERT-questions-classification

Attention Is All You Need

https://github.com/nguyenphamvan/BertSum-master

Text Summarization with Pretrained Encoders

https://github.com/niccolot/ENG-ITA_NMT

Attention Is All You Need

https://github.com/nicholasbao/nlp_job

Attention Is All You Need

https://github.com/nicklashansen/tdmpc2

TD-MPC2: Scalable, Robust World Models for Continuous Control

https://github.com/nicolaffETHZ/Brain2Word_paper

Neural Discrete Representation Learning

https://github.com/nietao2/DeepSpeechRecognition

Attention Is All You Need

https://github.com/nii-yamagishilab/ClassNSeg

Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos

https://github.com/nile649/POLY-GAN

Poly-GAN: Multi-Conditioned GAN for Fashion Synthesis

https://github.com/ninglab/M2

M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

https://github.com/nisharaichur/SegNet-Encoder-Decoder-Architecture-for-Image-Segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/nisharaichur/segNet_tensorflow

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/nithinksath96/Speech_Recognition

Listen, Attend and Spell

https://github.com/niyunsheng/ems-sd

Inference with Reference: Lossless Acceleration of Large Language Models

https://github.com/njunlp/mcsd

SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification

https://github.com/nkjsy/Neural-Machine-Translation

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

https://github.com/nku-hlt/promptrank

PromptRank: Unsupervised Keyphrase Extraction Using Prompt

https://github.com/nlinc1905/dsilt-tsa

Attention Is All You Need

https://github.com/nlmatics/llmsherpa

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

https://github.com/nlp-uoregon/famie

Deep Active Learning for Named Entity Recognition

https://github.com/nmfisher/bert-modified

Attention Is All You Need

https://github.com/nnizhang/VST

Visual Saliency Transformer

https://github.com/nobodyplayer1/vm-unetv2

U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

https://github.com/nolanliou/PeopleSegmentationDemo

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/noriyukipy/tfchat

Attention Is All You Need

https://github.com/noriyukipy/tfdlg

Attention Is All You Need

https://github.com/northeastsquare/bts

From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

https://github.com/nrc-cnrc/sockeye-multisource

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/nsfzyzz/generalization_metrics_for_nlp

Attention Is All You Need

https://github.com/nshaud/DeepNetsForEO

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/ntunlp/ptrnet-depparser

Hierarchical Pointer Net Parsing

https://github.com/nvinden/7ChannelEML

Contextual Encoder-Decoder Network for Visual Saliency Prediction

https://github.com/nvlabs/catk

Wayformer: Motion Forecasting via Simple & Efficient Attention Networks

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