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

https://github.com/jenninglim/multiscale-features

Kernel Stein Tests for Multiple Model Comparison

https://github.com/jeroneandrews-sony/cama

Conditional Adversarial Camera Model Anonymization

https://github.com/jim-schwoebel/allie

Model-based Asynchronous Hyperparameter and Neural Architecture Search

https://github.com/jingtaozhan/JPQ

Optimizing Dense Retrieval Model Training with Hard Negatives

https://github.com/jingtaozhan/LearningToRetrieve

Optimizing Dense Retrieval Model Training with Hard Negatives

https://github.com/jingtaozhan/repconc

Optimizing Dense Retrieval Model Training with Hard Negatives

https://github.com/jkooy/Parameter-efficient-Fine-tuning-for-Vision-Transformers

Parameter-efficient Model Adaptation for Vision Transformers

https://github.com/joe5saia/PanelVariationalReccurentAutoEncoder

A Recurrent Latent Variable Model for Sequential Data

https://github.com/jongpillee/deep-content-user

Deep Content-User Embedding Model for Music Recommendation

https://github.com/josephch405/jit-masker

Online Model Distillation for Efficient Video Inference

https://github.com/josh-tobin/egqn-datasets

ShapeNet: An Information-Rich 3D Model Repository

https://github.com/joshua-shuhan/diffri

Diffusion model for relational inference

https://github.com/js-aguiar/wheat-object-detection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/jump-dev/diffopt.jl

Flexible Differentiable Optimization via Model Transformations

https://github.com/junming-yang/mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/juntingzh/incremental-learning-baselines

Class-incremental Learning via Deep Model Consolidation

https://github.com/justinchiu/openlogprobs

Language Model Inversion

https://github.com/jwzhanggy/Graph-Bert

SEGEN: Sample-Ensemble Genetic Evolutional Network Model

https://github.com/jxu43/replication-mbpo

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/k1l1/SLT

FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction

https://github.com/kairess/efficientnet_example

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/kaist-vclab/sphere-stereo

The Double Sphere Camera Model

https://github.com/kamenbliznashki/pixel_models

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/karpathy/makemore

WaveNet: A Generative Model for Raw Audio

https://github.com/kaung-htet-myat/Multi-teachers-Knowledge-Distillation

Hydra: Preserving Ensemble Diversity for Model Distillation

https://github.com/kcrosley-leisurelabs/jukebox-reduce-log

Jukebox: A Generative Model for Music

https://github.com/kdha0727/lung-opacity-and-covid-chest-x-ray-detection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/keonlee9420/DiffSinger

DiffWave: A Versatile Diffusion Model for Audio Synthesis

https://github.com/kewin1807/table-recognition

Image-based table recognition: data, model, and evaluation

https://github.com/keyhsw/chemllm

ChemLLM: A Chemical Large Language Model

https://github.com/kheeong/XLM_OWN

Cross-lingual Language Model Pretraining

https://github.com/kidist-amde/ddro

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/kidist-amde/ddro-direct-document-relevance-optimization

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/kidswithtokens/medsegdiff

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/kingstarcraft/speech-to-text-wavenet2

WaveNet: A Generative Model for Raw Audio

https://github.com/kmeng01/rome

Fast Model Editing at Scale

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