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/yxuansu/contrastive_search_versus_contrastive_decoding

Contrastive Decoding: Open-ended Text Generation as Optimization

https://github.com/yxuansu/simctg

Contrastive Search Is What You Need For Neural Text Generation

https://github.com/yxzwang/normalized-information-payload

How to Train BERT with an Academic Budget

https://github.com/yydai/bert_test

Semi-supervised Sequence Learning

https://github.com/yynil/rwkvinllama

Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models

https://github.com/zapplea/bert

Semi-supervised Sequence Learning

https://github.com/zaradana/Fast_BERT

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/zbp-xxxp/R-Drop-Paddle

R-Drop: Regularized Dropout for Neural Networks

https://github.com/zebangcheng/emotion-llama

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

https://github.com/zechenli03/sensorllm

Chronos: Learning the Language of Time Series

https://github.com/zenanz/ChemPatentEmbeddings

Deep contextualized word representations

https://github.com/zerospeech/zerospeech2021_baseline

The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling

https://github.com/zfj1998/CodeBert-Code2Text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/zh320/realtime-semantic-segmentation-pytorch

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

https://github.com/zhang-huihui/git-repository

Semi-supervised Sequence Learning

https://github.com/zhanglab-aim/lingo

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

https://github.com/zhangyaoyuan/GAN-Simplification

Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

https://github.com/zhangyezhuo/mamca

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/zhaochongan/coseg

Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model

https://github.com/zhaohan-xi/plm-prompt-defense

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

https://github.com/zhaoolee/garss

AgentOps: Enabling Observability of LLM Agents

https://github.com/zhaozixiang1228/mmif-cddfuse

Image Fusion via Vision-Language Model

https://github.com/zhaozixiang1228/mmif-emma

Image Fusion via Vision-Language Model

https://github.com/zhdbwe/Paper-DailyReading

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/zhegan27/LXMERT-AdvTrain

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

https://github.com/zhegan27/VILLA

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

https://github.com/zhejz/hptr

Scene Transformer: A unified architecture for predicting multiple agent trajectories

https://github.com/zhejz/trafficbotsv1.5

Scene Transformer: A unified architecture for predicting multiple agent trajectories

https://github.com/zhen-tan-dmml/llm4annotation

A Survey of Large Language Models

https://github.com/zhengli97/promptkd

Cascade Prompt Learning for Vision-Language Model Adaptation

https://github.com/zhengxiangshi/powerfulpromptft

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

https://github.com/zhijing-jin/GEC_papers

The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction

https://github.com/zhl98/T2T_paddle

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

https://github.com/zhongping-zhang/engine

Pointer Sentinel Mixture Models

https://github.com/zhou059/w266-project

Quasi-Recurrent Neural Networks

https://github.com/zhu-0108/mts-unmixers

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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