SOTAVerified

Interpretability Techniques for Deep Learning

Papers

Showing 110 of 25 papers

TitleStatusHype
Time series saliency maps: explaining models across multiple domainsCode1
Dissecting and Mitigating Diffusion Bias via Mechanistic InterpretabilityCode1
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
CausalGym: Benchmarking causal interpretability methods on linguistic tasksCode2
Less is More: Fewer Interpretable Region via Submodular Subset SelectionCode2
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Improving Interpretability via Regularization of Neural Activation Sensitivity0
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence MeasureCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
Show:102550
← PrevPage 1 of 3Next →

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