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/JT-Sun/Filtering-WoRA

VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation

https://github.com/JakubStefko/w2vf

FRAGE: Frequency-Agnostic Word Representation

https://github.com/Jamie-Stirling/RetNet

Retentive Network: A Successor to Transformer for Large Language Models

https://github.com/Janus-Shiau/awd-lstm-tensorflow

Regularizing and Optimizing LSTM Language Models

https://github.com/Japanese-Image-Captioning/ClipCap-for-Japanese

ClipCap: CLIP Prefix for Image Captioning

https://github.com/Jellyfish042/RWKV_Othello

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues

https://github.com/JessikaSmith/language_model

Regularizing and Optimizing LSTM Language Models

https://github.com/JiachengLi1995/UCTopic

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

https://github.com/JianGoForIt/YellowFin

YellowFin and the Art of Momentum Tuning

https://github.com/JianGoForIt/YellowFin_Pytorch

Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling

https://github.com/Jiaxin-Ye/DepMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Jl-wei/ai-gen-ui

On AI-Inspired UI-Design

https://github.com/Jmkernes/PAR-Transformer-XL

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

https://github.com/JohannLee1996/bert

Semi-supervised Sequence Learning

https://github.com/JohnKurian/TableGPT

Few-Shot NLG with Pre-Trained Language Model

https://github.com/JonathanRaiman/tensorflow_qrnn

Quasi-Recurrent Neural Networks

https://github.com/Joppewouts/belabBERT

Text-based classification of interviews for mental health -- juxtaposing the state of the art

https://github.com/Joshuaoneheart/Fintech-Final-Report

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/JuanJoseMV/neuraltextgen

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

https://github.com/Julian1070/Deep-Learning

Universal Language Model Fine-tuning for Text Classification

https://github.com/JunhoKim94/MELT

MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science

https://github.com/JunnYu/paddle-mpnet

MPNet: Masked and Permuted Pre-training for Language Understanding

https://github.com/JunnYu/paddle_ctrl

CTRL: A Conditional Transformer Language Model for Controllable Generation

https://github.com/JunnYu/x-transformers-paddle

Stabilizing Transformers for Reinforcement Learning

https://github.com/JunnYu/xlm_paddle

Cross-lingual Language Model Pretraining

https://github.com/JunrQ/NAS

DARTS: Differentiable Architecture Search

https://github.com/KaenChan/ProbFace

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

https://github.com/KamalM8/Few-Shot-learning-Fashion

Matching Networks for One Shot Learning

https://github.com/Kamino666/S2VT-video-caption

Sequence to Sequence -- Video to Text

https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/Katsumata420/bert_mlm

Semi-supervised Sequence Learning

https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-

Semi-supervised Sequence Learning

https://github.com/KimJeongSun/SpecAugment_numpy_scipy

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/Kitsunetic/Optimizer-Visualization

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/KnightZhang625/BERT_TF

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/KnightZhang625/DARTS_NAS_TextClassification

DARTS: Differentiable Architecture Search

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