2 citations · 3 across the 4 of their papers we have counts for
4 papers
Nearly Optimal Bayesian Inference for Structural Missingness
Chen Liang, Donghua Yang, Yutong Zhao +9
Structural missingness breaks 'just impute and train': values can be undefined by causal or logical constraints, and the mask may depend on observed variables, unobserved variables…
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
Qingsong Lv, Jiasheng Sun, Sheng Zhou +6
To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolution…
A Comprehensive Study of Structural Pruning for Vision Models
Changhao Li, Haoling Li, Mengqi Xue +6
Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics,…
Why is the prediction wrong? Towards underfitting case explanation via meta-classification
Sheng Zhou, Pierre Blanchart, Michel Crucianu +1
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier. Sinc…