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

https://github.com/kowshikthopalli/decider

공식 코드

DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation

https://github.com/koyejo-lab/jointgraphicallasso

공식 코드

Joint Gaussian Graphical Model Estimation: A Survey

https://github.com/krishnap25/fl_partial_personalization

공식 코드

Federated Learning with Partial Model Personalization

https://github.com/ksandeshk/lens

공식 코드

Rethinking Robustness of Model Attributions

https://github.com/kugwzk/dide

공식 코드

Distilled Dual-Encoder Model for Vision-Language Understanding

https://github.com/kugwzk/distilled-dualencoder

공식 코드

Distilled Dual-Encoder Model for Vision-Language Understanding

https://github.com/kwai-kolors/lpo

공식 코드

Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level Preference Optimization

https://github.com/kxhit/EscherNet

공식 코드

EscherNet: A Generative Model for Scalable View Synthesis

https://github.com/laakeri/sharpsat-td

공식 코드

SharpSAT-TD in Model Counting Competitions 2021-2023

https://github.com/labshuhanggu/dcae

공식 코드

Learned Image Compression with Dictionary-based Entropy Model

https://github.com/lamda-bbo/offline-ram

공식 코드

Offline Model-Based Optimization by Learning to Rank

https://github.com/lancopku/SGM

공식 코드

SGM: Sequence Generation Model for Multi-label Classification

https://github.com/landskape-ai/reprogram_lt

공식 코드

Uncovering the Hidden Cost of Model Compression

https://github.com/langlrsw/MEED

공식 코드

Adversarial Infidelity Learning for Model Interpretation

https://github.com/lasgroup/alexp

공식 코드

Anytime Model Selection in Linear Bandits

https://github.com/lauramanduchi/DC-GMM

공식 코드

Deep Conditional Gaussian Mixture Model for Constrained Clustering

https://github.com/laurieburchell/open-lid-dataset

공식 코드

An Open Dataset and Model for Language Identification

https://github.com/lava-lab/ratm

공식 코드

Robust Active Measuring under Model Uncertainty

https://github.com/layer6ai-labs/ProxyFL

공식 코드

Decentralized Federated Learning through Proxy Model Sharing

https://github.com/leaplabthu/checkpointkd

공식 코드

Efficient Knowledge Distillation from Model Checkpoints

https://github.com/learningmatter-mit/uq_singlenn

공식 코드

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

https://github.com/ledgerinvesting/bayesblend

공식 코드

BayesBlend: Easy Model Blending using Pseudo-Bayesian Model Averaging, Stacking and Hierarchical Stacking in Python

https://github.com/leipingjie/adaptershadow

공식 코드

AdapterShadow: Adapting Segment Anything Model for Shadow Detection

https://github.com/leon-francis/multi-modal-stance-detection

공식 코드

Multi-modal Stance Detection: New Datasets and Model

https://github.com/leon022/edgepro

공식 코드

Edge Deep Learning Model Protection via Neuron Authorization

https://github.com/leondz/lm_risk_cards

공식 코드

Assessing Language Model Deployment with Risk Cards

https://github.com/leonty1/deepldnn

공식 코드

Efficient State Space Model via Fast Tensor Convolution and Block Diagonalization

https://github.com/leonty1/essm

공식 코드

Efficient State Space Model via Fast Tensor Convolution and Block Diagonalization

https://github.com/lf-wen/hgdm

공식 코드

Hyperbolic Graph Diffusion Model

https://github.com/lgessler/pronto

공식 코드

PrOnto: Language Model Evaluations for 859 Languages

https://github.com/li-hyn/llm_catastrophicforgetting

공식 코드

Revisiting Catastrophic Forgetting in Large Language Model Tuning

https://github.com/liaochunyang/DPRFM

공식 코드

Differentially Private Random Feature Model

https://github.com/lihaoxin2020/narrowbert

공식 코드

NarrowBERT: Accelerating Masked Language Model Pretraining and Inference

https://github.com/linanzhang/nonconvexsparsecyclicrecovery

공식 코드

Structured model selection via $\ell_1-\ell_2$ optimization

https://github.com/line/sacpo

공식 코드

Stepwise Alignment for Constrained Language Model Policy Optimization

https://github.com/linear95/rlm

공식 코드

Replacing Language Model for Style Transfer

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