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

https://github.com/mlvlab/llamo

공식 코드

LLaMo: Large Language Model-based Molecular Graph Assistant

https://github.com/mmjing/variational_model_perturbation

공식 코드

Variational Model Perturbation for Source-Free Domain Adaptation

https://github.com/mnoukhov/elastic-reset

공식 코드

Language Model Alignment with Elastic Reset

https://github.com/model-glue/model-glue

공식 코드

Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild

https://github.com/modeltc/mqbench

공식 코드

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

https://github.com/modelzoos/vitmodelzoo

공식 코드

A Model Zoo of Vision Transformers

https://github.com/mominabbass/sharp-maml

공식 코드

Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning

https://github.com/mononitogoswami/tsad-model-selection

공식 코드

Unsupervised Model Selection for Time-series Anomaly Detection

https://github.com/moranshkolnik/RobustQuantization

공식 코드

Robust Quantization: One Model to Rule Them All

https://github.com/moritzhein/robmpcexploitmon

공식 코드

Robust Model Predictive Control Exploiting Monotonicity Properties

https://github.com/mosamdabhi/3dlfm

공식 코드

3D-LFM: Lifting Foundation Model

https://github.com/motefly/LMA

공식 코드

Light Multi-segment Activation for Model Compression

https://github.com/mowillia/LigandReceptor

공식 코드

Derangement model of ligand-receptor binding

https://github.com/mozzitastebitter/medjex

공식 코드

MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score

https://github.com/mr-eggplant/eata

공식 코드

Efficient Test-Time Model Adaptation without Forgetting

https://github.com/mr-eggplant/foa

공식 코드

Test-Time Model Adaptation with Only Forward Passes

https://github.com/mrc-ide/referenced-TI

공식 코드

Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection

https://github.com/mrjleo/boilernet

공식 코드

Boilerplate Removal using a Neural Sequence Labeling Model

https://github.com/mshunya/affinetl

공식 코드

Transfer learning with affine model transformation

https://github.com/msu-mlsys-lab/fedrolex

공식 코드

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

https://github.com/mukobi/welfare-diplomacy

공식 코드

Welfare Diplomacy: Benchmarking Language Model Cooperation

https://github.com/murtyshikhar/languagepatching

공식 코드

Fixing Model Bugs with Natural Language Patches

https://github.com/muzairkhattak/promptsrc

공식 코드

Self-regulating Prompts: Foundational Model Adaptation without Forgetting

https://github.com/mzakizadeh/Gender-Encoding-Patterns

공식 코드

Gender Encoding Patterns in Pretrained Language Model Representations

https://github.com/mzeromiko/vmamba

공식 코드

VMamba: Visual State Space Model

https://github.com/nafissadeq/informask

공식 코드

InforMask: Unsupervised Informative Masking for Language Model Pretraining

https://github.com/nahidalam/maya

공식 코드

Maya: An Instruction Finetuned Multilingual Multimodal Model

https://github.com/naiyuyin/ICDH

공식 코드

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model

https://github.com/namisan/mt-dnn

공식 코드

A Hybrid Neural Network Model for Commonsense Reasoning

https://github.com/namkoong-lab/disde

공식 코드

Diagnosing Model Performance Under Distribution Shift

https://github.com/nanami18/snowballed_hallucination

공식 코드

How Language Model Hallucinations Can Snowball

https://github.com/nardien/kala

공식 코드

KALA: Knowledge-Augmented Language Model Adaptation

https://github.com/nasa-impact/gravity-wave-finetuning

공식 코드

Prithvi WxC: Foundation Model for Weather and Climate

https://github.com/nasa-impact/prithvi-wxc

공식 코드

Prithvi WxC: Foundation Model for Weather and Climate

https://github.com/naturomics/DLF

공식 코드

Generative Model with Dynamic Linear Flow

https://github.com/neil-dey/valid-inference-ml-estimators

공식 코드

Valid Inference for Machine Learning Model Parameters

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