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Sensitivity
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Shaier/question_repeat
공식 코드Asking Again and Again: Exploring LLM Robustness to Repeated Questions
https://github.com/Sisihawaii/Shiftable_electricity
공식 코드https://github.com/Sri-Harsha/scpp
공식 코드SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations
https://github.com/StijnVerdenius/SNIP-it
공식 코드https://github.com/TengMCing/auto_residual_reading_paper
공식 코드Automated Assessment of Residual Plots with Computer Vision Models
https://github.com/TurboFreeze/dp-robust-filter
공식 코드Designing Differentially Private Estimators in High Dimensions
https://github.com/WolfNiu/AdversarialDialogue
공식 코드Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models
https://github.com/YannisBilly/Evaluation-of-pretrained-language-models-on-music-understanding
공식 코드Evaluation of pretrained language models on music understanding
https://github.com/Youmna-H/coherence-analysis
공식 코드https://github.com/ZhaoqLiu/BreakNLI
공식 코드Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
https://github.com/aAbdz/DeepACSON
공식 코드Scattering approach to diffusion quantifies axonal damage in brain injury
https://github.com/aaltoml/periodicbnn
공식 코드https://github.com/aatlantise/syntactic-augmentation-nli
공식 코드Syntactic Data Augmentation Increases Robustness to Inference Heuristics
https://github.com/aburns4/UnsupDisent
공식 코드Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
https://github.com/ad-papers-material/bart_systreviewclassifier
공식 코드https://github.com/adpartin/dr-learning-curves
공식 코드Learning Curves for Drug Response Prediction in Cancer Cell Lines
https://github.com/agakshat/spacefortress
공식 코드Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a Benchmark
https://github.com/aisingapore/bhasa
공식 코드BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models
https://github.com/aklamun/reinsurance_networks
공식 코드https://github.com/aleesuss/c19
공식 코드Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images
https://github.com/alejandrods/Analysis-of-the-robustness-of-NMF-algorithms
공식 코드https://github.com/alestolfo/causal-math
공식 코드A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
https://github.com/alexander-rakhlin/ICIAR2018
공식 코드Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
https://github.com/alexandrepsq/information-restrited-nlms
공식 코드https://github.com/algue-rythme/lip-dp
공식 코드https://github.com/aliborji/ap_box_perturbation
공식 코드Sensitivity of Average Precision to Bounding Box Perturbations
https://github.com/alikaraali/dr-vnet
공식 코드DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet
https://github.com/allenai/abnirml
공식 코드https://github.com/alonhzn/testauc
공식 코드https://github.com/altsoph/artificial_degree_modifiers
공식 코드Connecting degree and polarity: An artificial language learning study
https://github.com/ambehroozi/sc_neural_operators
공식 코드https://github.com/ammarlodhi255/pediatric_wrist_abnormality_detection-end-to-end-implementation
공식 코드https://github.com/amri-lab/imjense
공식 코드https://github.com/andrew-draganov/fast-coreset-generation
공식 코드Settling Time vs. Accuracy Tradeoffs for Clustering Big Data
https://github.com/andrewherren/shap-anova-examples
공식 코드Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.