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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/anguyen8/sam

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SAM: The Sensitivity of Attribution Methods to Hyperparameters

https://github.com/anishazaveri/austen_plots

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Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

https://github.com/anmold-07/physics-guided-blackout-mitigation

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RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors

https://github.com/anndvision/overcast

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Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

https://github.com/apicis/aff-seg

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Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

https://github.com/arce-star/Bias-in-Decision-Making-for-AI-Ethical-Dilemmas--A-Comparative-Study-of-ChatGPT-and-Claude

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Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude

https://github.com/arlenejohn/CNN_sleep_apnea

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A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors

https://github.com/arpanmangal/CovidAID

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CovidAID: COVID-19 Detection Using Chest X-Ray

https://github.com/automl/SEARL

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Sample-Efficient Automated Deep Reinforcement Learning

https://github.com/babukumarannalini/sizingflexibility

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Does Oversizing Improve Prosumer Profitability in a Flexibility Market? -- A Sensitivity Analysis using PV-battery System

https://github.com/bakaburg1/baysren

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An open-source integrated framework for the automation of citation collection and screening in systematic reviews

https://github.com/basiralab/BrainNet-ML-ToolBox

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Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks

https://github.com/bayesflow-org/SA-ABI

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Sensitivity-Aware Amortized Bayesian Inference

https://github.com/bayesomicslab/brem

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Bayesian Reconstruction and Differential Testing of Excised mRNA

https://github.com/beinborn/brain-lang

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Robust Evaluation of Language-Brain Encoding Experiments

https://github.com/belakaria/al-gsa-dgsms

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Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes

https://github.com/bfava/IllusionOfIllusion

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The Illusion of the Illusion of Sparsity: An exercise in prior sensitivity

https://github.com/bogdan-kulynych/riskcal

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Attack-Aware Noise Calibration for Differential Privacy

https://github.com/bolinpan/coronaenet

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On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector

https://github.com/bozheng-hit/xTune

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Consistency Regularization for Cross-Lingual Fine-Tuning

https://github.com/brianliu2/ABCSMC-SA

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Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis

https://github.com/bridgesra/active-manifold-icml2019-code

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Active Manifolds: A non-linear analogue to Active Subspaces

https://github.com/cagatayyildiz/npde

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Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching

https://github.com/carloscinelli/dml.sensemakr

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Long Story Short: Omitted Variable Bias in Causal Machine Learning

https://github.com/cat538/mxmoe

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MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design

https://github.com/causalml/doublyvaliddoublysharpsensitivity

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Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding

https://github.com/cbhu523/chatgpt_ASD_diagnosis

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Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features

https://github.com/celin-yao/da-moe

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DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts

https://github.com/chanliang/orig

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Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization

https://github.com/chihkuanyeh/saliency_evaluation

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On the (In)fidelity and Sensitivity for Explanations

https://github.com/chingyaoc/mdm

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The Role of Embedding Complexity in Domain-invariant Representations

https://github.com/chkern/tweet-annotation-sensitivity

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Annotation Sensitivity: Training Data Collection Methods Affect Model Performance

https://github.com/cleverhans-lab/gradients-look-alike-sensitivity-is-often-overestimated-in-dp-sgd

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Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD

https://github.com/cliang1453/sage

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No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models

https://github.com/cmaldonado1/predicting-cvd-using-oct-images

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Predicting risk of cardiovascular disease using retinal OCT imaging

https://github.com/cmd2001/KVTuner

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KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference

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