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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/evtaktasheva/dependency_extraction

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Shaking Syntactic Trees on the Sesame Street: Multilingual Probing with Controllable Perturbations

https://github.com/explainingai/stability

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Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity

https://github.com/exploration-lab/eticor

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EtiCor: Corpus for Analyzing LLMs for Etiquettes

https://github.com/exploration-lab/eticor-plus-plus

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EtiCor++: Towards Understanding Etiquettical Bias in LLMs

https://github.com/facebookresearch/multicontrievers-analysis

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MultiContrievers: Analysis of Dense Retrieval Representations

https://github.com/fbargaglistoffi/sensitivity-analysis-machine-learning

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Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy

https://github.com/fbarraquand/sensitivity_stochrma

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No sensitivity to functional forms in the Rosenzweig-MacArthur model with strong environmental stochasticity

https://github.com/fe1ixxu/intra-distillation

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The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains

https://github.com/federhub/pyGRNN

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On Feature Selection Using Anisotropic General Regression Neural Network

https://github.com/feedbackward/bdd

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Flexible risk design using bi-directional dispersion

https://github.com/fel-thomas/Sobol-Attribution-Method

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Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis

https://github.com/felipe-gomez/riskcal

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Attack-Aware Noise Calibration for Differential Privacy

https://github.com/flame-chasers/rasa

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RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search

https://github.com/fmthoker/severe-benchmark

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How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?

https://github.com/ftramer/Excessive-Invariance

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Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

https://github.com/fuchuanqu/gianthunter

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GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search

https://github.com/fudavd/revolve

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Comparing lifetime learning methods for morphologically evolving robots

https://github.com/fundamentalvision/siamese-image-modeling

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Siamese Image Modeling for Self-Supervised Vision Representation Learning

https://github.com/fzimmermann89/cmrxrecon

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NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps

https://github.com/gaiters-aerials/player_fl

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

https://github.com/garygsw/smooth-taylor

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Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution

https://github.com/geoffbacon/does-bert-agree

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Does BERT agree? Evaluating knowledge of structure dependence through agreement relations

https://github.com/giskard-ai/phare

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Phare: A Safety Probe for Large Language Models

https://github.com/gmum/cassle

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Augmentation-aware Self-supervised Learning with Conditioned Projector

https://github.com/goldengua/counterfactual_inference_lm

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Counterfactual reasoning: Do language models need world knowledge for causal understanding?

https://github.com/gollini/fisher_taylor_sensitivity

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Fishing For Cheap And Efficient Pruners At Initialization

https://github.com/google-research/google-research/tree/master/private_personalized_pagerank

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Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

https://github.com/google-research/torchsde

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Scalable Gradients for Stochastic Differential Equations

https://github.com/gp1d/fuzzyROC

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The Fuzzy ROC

https://github.com/gpeyre/2017-SIOPT-stratification

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Sensitivity Analysis for Mirror-Stratifiable Convex Functions

https://github.com/graph-com/node_dp

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Differentially Private Relational Learning with Entity-level Privacy Guarantees

https://github.com/grushaprasad/RNN-Priming

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Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models

https://github.com/gt-ripl/geometric-sensitivity-decomposition

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A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition

https://github.com/gtfactslab/evcharging_sensitivity

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Sensitivity to User Mischaracterizations in Electric Vehicle Charging

https://github.com/gwastro/bns-machine-learning-search

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Detection of gravitational-wave signals from binary neutron star mergers using machine learning

https://github.com/h-sax/cmsb-2023

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New Perspectives on Sensitivity and Identifiability Analysis using the Unscented Kalman Filter

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