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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mandyyyyii/scibench
공식 코드SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
https://github.com/maoyj1998/transdic
공식 코드https://github.com/marcelsheeny/radiate_sdk
공식 코드RADIATE: A Radar Dataset for Automotive Perception in Bad Weather
https://github.com/marcovirgolin/GP-GOMEA
공식 코드Contemporary Symbolic Regression Methods and their Relative Performance
https://github.com/marct0k/franck
공식 코드Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning
https://github.com/marialaurasantoni/diversity-fitness
공식 코드Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
https://github.com/marieoestreich/pro-gene-gen
공식 코드Towards Biologically Plausible and Private Gene Expression Data Generation
https://github.com/mariotheone/gretel
공식 코드https://github.com/markus93/fit-on-the-test
공식 코드On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers
https://github.com/marrlab/domainlab
공식 코드DomainLab: A modular Python package for domain generalization in deep learning
https://github.com/marsggbo/CovidNet3D
공식 코드https://github.com/martinbeseda/steady-state
공식 코드A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
https://github.com/martinjurkovic/syntherela
공식 코드Benchmarking the Fidelity and Utility of Synthetic Relational Data
https://github.com/martinmenten/skeletonization-for-gradient-based-optimization
공식 코드https://github.com/martinzaefferer/COBBS
공식 코드https://github.com/martinzaefferer/zaef20b
공식 코드https://github.com/marvinlvn/babyslm
공식 코드BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
https://github.com/mary-paterson/laryngealcancerclassificationbenchmark
공식 코드https://github.com/maryammaryam877/genai_tigs
공식 코드Benchmarking Generative AI Models for Deep Learning Test Input Generation
https://github.com/mateoespinosa/concept-quality
공식 코드Towards Robust Metrics for Concept Representation Evaluation
https://github.com/matt-baugh/nnood
공식 코드nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
https://github.com/mattbierbaum/arxiv-public-datasets
공식 코드https://github.com/matteospanio/torchfx
공식 코드TorchFX: A modern approach to Audio DSP with PyTorch and GPU acceleration
https://github.com/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/matthewcym/voicebench
공식 코드https://github.com/matthias-wright/art-fid
공식 코드https://github.com/mattsheng/PAN_SR
공식 코드Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
https://github.com/matus-pikuliak/genderbench
공식 코드https://github.com/maximilianb2/pc-gym
공식 코드https://github.com/maxium0526/category-wise-fine-tuning
공식 코드https://github.com/mazpie/genrl
공식 코드GenRL: Multimodal-foundation world models for generalization in embodied agents
https://github.com/mazqtpopx/cranfield-navigation-gym
공식 코드Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments
https://github.com/mberr/ea-sota-comparison
공식 코드A Critical Assessment of State-of-the-Art in Entity Alignment
https://github.com/mboudiaf/TIM
공식 코드https://github.com/mboudiaf/pytorch-meta-dataset
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.