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

https://github.com/sgl-project/sglang

공식 코드

SGLang: Efficient Execution of Structured Language Model Programs

https://github.com/shagunsodhani/consistent-dynamics

공식 코드

Learning Powerful Policies by Using Consistent Dynamics Model

https://github.com/shahaffind/GMRF-MM

공식 코드

Effective Learning of a GMRF Mixture Model

https://github.com/shallowlearner93/neurodavis

공식 코드

NeuroDAVIS: A neural network model for data visualization

https://github.com/shamindras/bttv-aistats2020

공식 코드

Nonparametric Estimation in the Dynamic Bradley-Terry Model

https://github.com/shampj/het-fl

공식 코드

Towards Model-Agnostic Federated Learning over Networks

https://github.com/shanface33/deepfake_model_attribution

공식 코드

Model Attribution of Face-swap Deepfake Videos

https://github.com/shaofeishen768/camu

공식 코드

CaMU: Disentangling Causal Effects in Deep Model Unlearning

https://github.com/shaolinbit/GraphicalStateSpaceModel

공식 코드

Graphical State Space Model

https://github.com/shaoruchen/lumped-uncertainty-sls-mpc

공식 코드

Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis

https://github.com/shaoruchen/polytopic-slsmpc

공식 코드

Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis

https://github.com/sharad461/MKA-hallucination

공식 코드

MKA: Leveraging Cross-Lingual Consensus for Model Abstention

https://github.com/shark0-0/vg4d

공식 코드

VG4D: Vision-Language Model Goes 4D Video Recognition

https://github.com/shereen-elsayed/casm

공식 코드

Context-Aware Sequential Model for Multi-Behaviour Recommendation

https://github.com/shi-labs/disparse-multitask-model-compression

공식 코드

DiSparse: Disentangled Sparsification for Multitask Model Compression

https://github.com/shi-yu-wang/pgd-vae

공식 코드

Deep Generative Model for Periodic Graphs

https://github.com/shizhouxing/Attack-LM-Detectors

공식 코드

Red Teaming Language Model Detectors with Language Models

https://github.com/shlezingerlab/mbdl_book

공식 코드

Model-Based Deep Learning

https://github.com/shn66/Multi-Objective-LMPC

공식 코드

Multi-Objective Learning Model Predictive Control

https://github.com/shn66/rolmpc

공식 코드

Robust Output-Lifted Learning Model Predictive Control

https://github.com/shoufachen/diffusiondet

공식 코드

DiffusionDet: Diffusion Model for Object Detection

https://github.com/shuangtongli/scool

공식 코드

Structured Cooperative Learning with Graphical Model Priors

https://github.com/shuohangwang/SeqMatchSeq

공식 코드

A Compare-Aggregate Model for Matching Text Sequences

https://github.com/siatmmlab/awesome-diffusion-model-based-image-editing-methods

공식 코드

Diffusion Model-Based Image Editing: A Survey

https://github.com/sibirbil/smb

공식 코드

Bolstering Stochastic Gradient Descent with Model Building

https://github.com/sidak/otfusion

공식 코드

Model Fusion via Optimal Transport

https://github.com/siemens/industrialbenchmark

공식 코드

Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning

https://github.com/simofoti/localeigenprojdisentangled

공식 코드

3D Generative Model Latent Disentanglement via Local Eigenprojection

https://github.com/simon-tan/laplace

공식 코드

LaPLACE: Probabilistic Local Model-Agnostic Causal Explanations

https://github.com/sisl/MPHRL

공식 코드

Model Primitive Hierarchical Lifelong Reinforcement Learning

https://github.com/sisl/modelbasedvalidationinference

공식 코드

Model-based Validation as Probabilistic Inference

https://github.com/sisl/mpopis

공식 코드

Model Predictive Optimized Path Integral Strategies

https://github.com/sjtuplayer/few-shot-diffusion

공식 코드

Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption

https://github.com/skolouri/swae

공식 코드

Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

https://github.com/skyworkai/matrix-game

공식 코드

Matrix-Game: Interactive World Foundation Model

https://github.com/skyworkai/skyreels-v2

공식 코드

SkyReels-V2: Infinite-length Film Generative Model

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