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Language Modeling

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

https://github.com/zhuiyitechnology/gau-alpha

Transformer Quality in Linear Time

https://github.com/zhuzilin/ring-flash-attention

The Llama 3 Herd of Models

https://github.com/zhyack/UDRG

A Semi-Supervised Approach for Low-Resourced Text Generation

https://github.com/zi-lin/on-lstm-tensorflow

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

https://github.com/zijieli-Jlee/Dual-Diffusion

Dual Diffusion for Unified Image Generation and Understanding

https://github.com/ziye2chen/LLMs-for-Mathematical-Analysis

The Llama 3 Herd of Models

https://github.com/ziyizhang27/sdpo

REBEL: Reinforcement Learning via Regressing Relative Rewards

https://github.com/zjukg/maco

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

https://github.com/zjukg/mygo

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

https://github.com/zjukg/snag

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

https://github.com/zjunlp/promptkgc

LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings

https://github.com/znhy1024/protoco

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/zpc-666/Paddle-R-Drop

R-Drop: Regularized Dropout for Neural Networks

https://github.com/zphang/bert_on_stilts

Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks

https://github.com/zphang/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/zqhang/Accurate-WinCLIP-pytorch

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

https://github.com/zrrskywalker/point-bind

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/zs1314/microscopic-mamba

VMamba: Visual State Space Model

https://github.com/zs1314/octamamba

VMamba: Visual State Space Model

https://github.com/zs1314/skinmamba

VMamba: Visual State Space Model

https://github.com/zsweet/BERT_zsw

Semi-supervised Sequence Learning

https://github.com/ztcoalson/if-guide

Studying Large Language Model Generalization with Influence Functions

https://github.com/zxhhh97/ABot

DeepWalk: Online Learning of Social Representations

https://github.com/zysite/post

Empower Sequence Labeling with Task-Aware Neural Language Model

https://github.com/zziz/pwc

End-to-end Audio-visual Speech Recognition with Conformers

https://github.com/zzmtsvv/mamba-interface

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/zzmtsvv/rl_task

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://gitlab.com/birder/birder

iBOT: Image BERT Pre-Training with Online Tokenizer

https://gitlab.com/bpaassen/reservoir_stack_machines

Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages

https://gitlab.com/grounded-sport-convai/goal-baselines

HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

https://huggingface.co/datasets/alykassem/DSC-Benchmark

How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities

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