SOTAVerified

Interpretability Techniques for Deep Learning

Papers

Showing 125 of 25 papers

TitleStatusHype
Less is More: Fewer Interpretable Region via Submodular Subset SelectionCode2
CausalGym: Benchmarking causal interpretability methods on linguistic tasksCode2
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence MeasureCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierCode1
A Unified Approach to Interpreting Model PredictionsCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Dissecting and Mitigating Diffusion Bias via Mechanistic InterpretabilityCode1
Time series saliency maps: explaining models across multiple domainsCode1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
Axiomatic Attribution for Deep NetworksCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
RISE: Randomized Input Sampling for Explanation of Black-box ModelsCode1
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life PredictionCode0
Contextual Explanation NetworksCode0
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency MapsCode0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
What Do Compressed Deep Neural Networks Forget?Code0
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics0
A deep supervised learning approach for condition-based maintenance of naval propulsion systems Tarek0
Improving Interpretability via Regularization of Neural Activation Sensitivity0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DASLog odds-ratio (pythia-6.9b)9.95Unverified
2Linear probeLog odds-ratio (pythia-6.9b)3.42Unverified
3Difference-in-meansLog odds-ratio (pythia-6.9b)2.91Unverified
4k-meansLog odds-ratio (pythia-6.9b)1.87Unverified
5PCALog odds-ratio (pythia-6.9b)1.81Unverified
6LDALog odds-ratio (pythia-6.9b)0.27Unverified
7RandomLog odds-ratio (pythia-6.9b)0.01Unverified
#ModelMetricClaimedVerifiedStatus
1RISEInsertion AUC score0.57Unverified
2HSIC-AttributionInsertion AUC score0.57Unverified
3Kernel SHAPInsertion AUC score0.52Unverified
4LIMEInsertion AUC score0.52Unverified
5SaliencyInsertion AUC score0.46Unverified
6Grad-CAMInsertion AUC score0.37Unverified
7Integrated GradientsInsertion AUC score0.36Unverified