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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/connectomicslab/aneurysm_detection

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Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge

https://github.com/crgagne/twosteps_neurips2021

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Two steps to risk sensitivity

https://github.com/cviviers/3D_UNetSecondaryFeatures

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Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features

https://github.com/cvl-umass/COSE

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COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification

https://github.com/cvvletter/palsy

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Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features

https://github.com/cvxgrp/joint-lrsm

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Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models

https://github.com/danawr/adversarial_attacks_and_sparse_representations

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On The Relationship Between Universal Adversarial Attacks And Sparse Representations

https://github.com/danielcsaba/alpha_RI

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Attention elasticities and invariant information costs

https://github.com/danielracz/tansens_public

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Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle

https://github.com/danruod/fs-dgpm

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Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning

https://github.com/data-attribution-hp/data-attribution-hp

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Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining

https://github.com/data-sciencehub/gpce-sensitivity

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Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models

https://github.com/dccsillag/experiments-causal-generalization-bounds

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Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis

https://github.com/dch0319/glkm-deblur

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Generative Latent Kernel Modeling for Blind Motion Deblurring

https://github.com/dennisfrauen/neuralcsa

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A Neural Framework for Generalized Causal Sensitivity Analysis

https://github.com/dgl-prc/m_testing_adversatial_sample

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Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

https://github.com/dial-rpi/spectral-adversarial-data-augmentation

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When Neural Networks Fail to Generalize? A Model Sensitivity Perspective

https://github.com/diazf/pref_eval

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Recall, Robustness, and Lexicographic Evaluation

https://github.com/digirak/NathRanga2024

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Machine learning for exoplanet detection in high-contrast spectroscopy Combining cross correlation maps and deep learning on medium-resolution integral-field spectra

https://github.com/dipkamal/pasa

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PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis

https://github.com/dipuk0506/uq

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Uncertainty Aware Neural Network from Similarity and Sensitivity

https://github.com/divyam3897/mri-robustness

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On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis

https://github.com/docbrisky/coronary-occlusion

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Augmenting expert detection of early coronary artery occlusion from 12 lead electrocardiograms using deep learning

https://github.com/doerlbh/topologicalrsa

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The Topology and Geometry of Neural Representations

https://github.com/droodman/duflo-2001

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Schooling and Labor Market Consequences of School Construction in Indonesia: Comment

https://github.com/drugilsberg/paccmann

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PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

https://github.com/easonnie/analyze-compositionality-sensitivity-NLI

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Analyzing Compositionality-Sensitivity of NLI Models

https://github.com/eisape/incremental_parse_probe

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Probing for Incremental Parse States in Autoregressive Language Models

https://github.com/eit-nlp/multiconir

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MultiConIR: Towards multi-condition Information Retrieval

https://github.com/eladgsofer/adversarial_updated

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers

https://github.com/eladromanov/randomized-svd-code

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On the Noise Sensitivity of the Randomized SVD

https://github.com/elopezfune/urysohn-s-binary-classifier

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Leveraging the Urysohn Lemma of Topology for an Enhanced Binary Classifier

https://github.com/emmaaholmes/econ-climate-sensitivity

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Sensitivity analysis of an integrated climate-economic model

https://github.com/emory-aims/dpar

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DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy

https://github.com/espressoshock/pytorch-expected-gradcam

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Expected Grad-CAM: Towards gradient faithfulness

https://github.com/evangeliazve/topic_style_embeddings_dispersion

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Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models

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