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

https://github.com/lvweiwolf/efficientdet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/lvwerra/trl

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

https://github.com/lynetcha/completion3d

ShapeNet: An Information-Rich 3D Model Repository

https://github.com/lyqcom/efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/lyqcom/rexnet

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/lyronctk/quant-noisier-speech

Training with Quantization Noise for Extreme Model Compression

https://github.com/lzhengchun/nntrainflow

fairDMS: Rapid Model Training by Data and Model Reuse

https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion

Label-Only Model Inversion Attacks via Boundary Repulsion

https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/macournoyer/neuralconvo

A Neural Conversational Model

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

Reasoning with Language Model is Planning with World Model

https://github.com/mamadou-keita/vlm-detect

Towards the Detection of Diffusion Model Deepfakes

https://github.com/manshri/tesum

A Deep Reinforced Model for Abstractive Summarization

https://github.com/maragori/DeepfakeForensics-v1

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/marcointrovigne/WeatherDetection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/markWJJ/Multitask-Learning

Model-Protected Multi-Task Learning

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

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/martin-danelljan/ECO

Learning Discriminative Model Prediction for Tracking

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

Dirichlet Diffusion Score Model for Biological Sequence Generation

https://github.com/matteo-rizzo/explainable-fruit-ripeness-classification

A Unified Approach to Interpreting Model Predictions

https://github.com/mattiasxu/VQVAE-2

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/mattiasxu/Video-VQVAE

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/maverickren/pixellm

PixelLM: Pixel Reasoning with Large Multimodal Model

https://github.com/maxwelltsai/DeepGalaxy

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mazrk7/discvae

A Recurrent Latent Variable Model for Sequential Data

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

Repetition Improves Language Model Embeddings

https://github.com/meelgroup/approxmc

Rounding Meets Approximate Model Counting

https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mehdimashayekhi/Some-RL-Implementation

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/merazlab/3D_Deep_Learning_Link

ShapeNet: An Information-Rich 3D Model Repository

https://github.com/mgalley/DSTC7-End-to-End-Conversation-Modeling

A Knowledge-Grounded Neural Conversation Model

https://github.com/microsoft/MT-DNN

A Hybrid Neural Network Model for Commonsense Reasoning

https://github.com/microsoft/mattergen

MatterGen: a generative model for inorganic materials design

https://github.com/microsoft/msrflute

Privacy-Preserving News Recommendation Model Learning

https://github.com/microsoft/unicl

Florence: A New Foundation Model for Computer Vision

https://github.com/midimusicgeneration/LSTMgen

DeepBach: a Steerable Model for Bach Chorales Generation

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