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/deep-agent/r1-v

G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model

https://github.com/deepmind/optax

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

https://github.com/deepmipt/bert/tree/feat/multi_gpu

Semi-supervised Sequence Learning

https://github.com/deepseek-ai/dualpipe

DeepSeek-V3 Technical Report

https://github.com/deepset-ai/FARM

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/deepset-ai/haystack

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/denizyuret/fastsubs-googlecode

FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model

https://github.com/derlin/phrasal

Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German

https://github.com/deterministic-algorithms-lab/Large-XLM

Cross-lingual Language Model Pretraining

https://github.com/devanshbatra04/Named-Entity-Language-Models

Building Language Models for Text with Named Entities

https://github.com/devhemza/BERTweet_sentiment_analysis

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/devraj89/Deep-Learning-Resources

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

https://github.com/dfan/single-image-surface-normal-estimation

Improving Generalization Performance by Switching from Adam to SGD

https://github.com/dhanya-e/Google_Indic

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

https://github.com/dhecloud/simple_language_modelling

Recurrent Neural Network Regularization

https://github.com/dheerajrajagopal/EIGEN

CURIE: An Iterative Querying Approach for Reasoning About Situations

https://github.com/dhiraa/tener

BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding

https://github.com/dhruvramani/Transformers-RL

Stabilizing Transformers for Reinforcement Learning

https://github.com/dhruvsawhney/CS152_FinalProject

Universal Language Model Fine-tuning for Text Classification

https://github.com/dicarlolab/archconvnets

Neural Architecture Optimization

https://github.com/diff7/DARTS-devices

DARTS: Differentiable Architecture Search

https://github.com/dig-team/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/digirl-agent/digirl

CogAgent: A Visual Language Model for GUI Agents

https://github.com/divelab/vqa-text

Grounding of Textual Phrases in Images by Reconstruction

https://github.com/dltnwl/Korean-Speech-recognition

Listen, Attend and Spell

https://github.com/dmirlab-group/sadga

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

https://github.com/dmis-lab/olaph

Large language model validity via enhanced conformal prediction methods

https://github.com/dmis-lab/temporalhead

Knowledge Circuits in Pretrained Transformers

https://github.com/dmlc/dgl/blob/master/examples/README.md

Order Matters: Sequence to sequence for sets

https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer

BP-Transformer: Modelling Long-Range Context via Binary Partitioning

https://github.com/dmlc/gluon-nlp

Exploring the Limits of Language Modeling

https://github.com/dngback/co-forget-protocol

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/doc-doc/NExT-OE

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/docling-project/docling

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

https://github.com/domluna/memn2n

End-To-End Memory Networks

https://github.com/dongjun-Lee/transfer-learning-text-tf

Semi-supervised Sequence Learning

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