https://github.com/qbetterk/VarBench
공식 코드VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation
Papers with Code 아카이브
Data-free Knowledge Distillation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/qcri/llmebench
공식 코드LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
https://github.com/qhuni/deve-qa
공식 코드https://github.com/qianjiangcn/eh-dnas
공식 코드EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search
https://github.com/qianlong0502/DKDM
공식 코드DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture
https://github.com/qijiang-qj/scimf
공식 코드https://github.com/qing-yuan233/RMCBench
공식 코드RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code
https://github.com/qiufan319/benchmark_pc_attack
공식 코드https://github.com/qiuhuachuan/interactive-agents
공식 코드https://github.com/qiuyu96/carver
공식 코드Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
https://github.com/qsong-github/scdrugmap
공식 코드scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction
https://github.com/quark-framework/quark
공식 코드Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
https://github.com/qub-blesson/Scission
공식 코드Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks
https://github.com/quel-imaging/quel-qal
공식 코드https://github.com/quiil/benchmarkingpathologyfoundationmodels
공식 코드Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios
https://github.com/qunhualilab/llm-ds-reproducibility
공식 코드An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science
https://github.com/quqxui/medlasa
공식 코드Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
https://github.com/qutech-delft/qgym
공식 코드qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
https://github.com/rabbit-magic-wh/InDL
공식 코드InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion
https://github.com/radzim/obfuscated
공식 코드https://github.com/ragib-amin-nihal/c2a
공식 코드https://github.com/ragmeh11/qu-brats
공식 코드https://github.com/rahulsrma26/phonetic-word-embedding
공식 코드https://github.com/raiiyf/diffusets_exp
공식 코드https://github.com/rainavyas/attack-comparative-assessment
공식 코드Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
https://github.com/rainysponge/split-learning-attacks
공식 코드https://github.com/ralschoop/deepus
공식 코드https://github.com/ralucaj/dermx-benchmark
공식 코드Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks
https://github.com/rampasek/HGNet
공식 코드Hierarchical graph neural nets can capture long-range interactions
https://github.com/raphaelmemmesheimer/simitate
공식 코드https://github.com/raulsenaferreira/prdc_2021_sut_module
공식 코드Benchmarking Safety Monitors for Image Classifiers with Machine Learning
https://github.com/raymond-qiancx/dyncim
공식 코드DynCIM: Dynamic Curriculum for Imbalanced Multimodal Learning
https://github.com/rbrandt1/precise-benchmarking-of-xai
공식 코드https://github.com/rcap107/retrieve-merge-predict
공식 코드https://github.com/rcap107/starmie
공식 코드https://github.com/rcap107/yadl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.