Papers with Code 아카이브

Language Modeling

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코드

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

https://github.com/m-pana/spk_anon_nac_lm

Speaker anonymization using neural audio codec language models

https://github.com/m42-health/med42

Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

https://github.com/ma921/XRDidentifier

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

https://github.com/magic-lantern/nlp-transfer-learning

Universal Language Model Fine-tuning for Text Classification

https://github.com/magicyang1573/llm-hardware-test-generation

VerilogReader: LLM-Aided Hardware Test Generation

https://github.com/mahdip72/InstructPix2Pix

InstructPix2Pix: Learning to Follow Image Editing Instructions

https://github.com/maheshkaran/Nervana-Distiller

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

https://github.com/maitrix-org/llm-reasoners

Reasoning with Language Model is Planning with World Model

https://github.com/makcedward/nlpaug

Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations

https://github.com/makeyourownmaker/mixup

Regularizing Neural Networks by Penalizing Confident Output Distributions

https://github.com/mamamot/Russian-ULMFit

Regularizing and Optimizing LSTM Language Models

https://github.com/marka17/digit-recognition

Jasper: An End-to-End Convolutional Neural Acoustic Model

https://github.com/martaskrt/qdeq

Deep Equilibrium Models

https://github.com/martiansideofthemoon/relic-retrieval

ELI5: Long Form Question Answering

https://github.com/martin-gorner/tensorflow-rnn-shakespeare

Recurrent Neural Network Regularization

https://github.com/martincjespersen/DaAnonymization

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/marynader6/espnetV.8

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

https://github.com/marynader6/transformerTTS

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

https://github.com/masa-ue/svdd

Simple and Effective Masked Diffusion Language Models

https://github.com/mast-group/OpenVocabCodeNLM

Maybe Deep Neural Networks are the Best Choice for Modeling Source Code

https://github.com/materialsvirtuallab/megnet

Order Matters: Sequence to sequence for sets

https://github.com/maxtimer97/ssm-inspired-lif

Efficiently Modeling Long Sequences with Structured State Spaces

https://github.com/maxudong1993/mybert

Semi-supervised Sequence Learning

https://github.com/mayankk6196/darts-python

DARTS: Differentiable Architecture Search

https://github.com/mayubo2333/paie

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

https://github.com/mayurnewase/Quora-Bert

Semi-supervised Sequence Learning

https://github.com/mbzuai-clear/ioe-prompting

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat

Language Models are Few-Shot Learners

https://github.com/mcgill-nlp/llm2vec

Repetition Improves Language Model Embeddings

https://github.com/mdaniluk/language-detector

Universal Language Model Fine-tuning for Text Classification

https://github.com/mdeff/paper-cnn-graph-recurrent-iclr2017

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

https://github.com/mediabrain-sjtu/medicalgpt-zh

MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

https://github.com/megagonlabs/rotom

Data Augmentation using Pre-trained Transformer Models

https://github.com/mehrdadyo/Coherent-Language-Generator

Improving Language Generation with Sentence Coherence Objective

https://github.com/meizi1114/bert

Semi-supervised Sequence Learning

https://github.com/menajosep/AleatoricSent

Deep contextualized word representations

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