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Found 4 papers, 1 papers with code
Analyzing Explainer Robustness via Lipschitzness of Prediction Functions
We provide lower bound guarantees on the astuteness of a variety of explainers (e. g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function.
Deep Markov Spatio-Temporal Factorization
This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal.
Deep Kernel Learning for Clustering
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data.
Neural Topographic Factor Analysis for fMRI Data
Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli.
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