Papers with Code 아카이브

Language Modeling

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

https://github.com/jlamprou/Infini-Attention

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

https://github.com/jmichaelov/does-surprisal-explain-n400

Exploring the Limits of Language Modeling

https://github.com/jmtomczak/vae_householder_flow

Generating Sentences from a Continuous Space

https://github.com/joelweber97/Python3_TF_Certificate

Universal Language Model Fine-tuning for Text Classification

https://github.com/joeynmt/joeynmt

Sequence-to-Sequence Learning as Beam-Search Optimization

https://github.com/joez17/chatbridge

ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst

https://github.com/johngull/gradinit

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training

https://github.com/johnhw/differentiable_sorting

A Fully Differentiable Beam Search Decoder

https://github.com/johnma2006/mamba-minimal

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/jojonki/MemoryNetworks

End-To-End Memory Networks

https://github.com/jonahwinninghoff/Text-Summarization

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/jonjoncardoso/jonjoncardoso

Very Large Language Model as a Unified Methodology of Text Mining

https://github.com/jonny4929/PaddleSeg_espnetv2

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/josephch405/curriculum-nmt

Improving Neural Machine Translation Models with Monolingual Data

https://github.com/joshuaWang-bit/Textclassification-pytorch

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/jowitapodolak/finetuning_BERT_NER

How multilingual is Multilingual BERT?

https://github.com/joytianya/google_bert

Semi-supervised Sequence Learning

https://github.com/jquesnelle/scaled-rope

SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

https://github.com/jquesnelle/yarn

SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

https://github.com/jsbaan/DPAC-DialogueGAN

DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text

https://github.com/jsie7/ssm-benchmark

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/jsleep/wav2mid

Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

https://github.com/jsuarez5341/Efficient-Dynamic-Batching

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

https://github.com/jubgjf/plmtuningcompetition

Black-Box Tuning for Language-Model-as-a-Service

https://github.com/juletx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/juliakreutzer/joeynmt

Sequence-to-Sequence Learning as Beam-Search Optimization

https://github.com/jumon/espnet-1

Improved training of end-to-end attention models for speech recognition

https://github.com/jundaf2/INT8-Flash-Attention-FMHA-Quantization

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

https://github.com/junhahyung/bert_finetune

Semi-supervised Sequence Learning

https://github.com/junhahyung/bert_transfer

Semi-supervised Sequence Learning

https://github.com/junshutang/Make-It-3D

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/juruobenruo/DexVLA

DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control

https://github.com/justinchiu/openlogprobs

Language Model Inversion

https://github.com/justld/EspnetV2_paddle

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/juyongjiang/codeup

A Survey of Large Language Models

https://github.com/jvbalen/autoencoders_cf

Variational Autoencoders for Collaborative Filtering

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