Lapuschkin, S.
* 2016: Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
* 2017: Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition
* 2017: Understanding and Comparing Deep Neural Networks for Age and Gender Classification
* 2021: Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
* 2021: Explanation-Guided Training for Cross-Domain Few-Shot Classification
* 2021: Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
* 2021: Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
* 2022: Measurably Stronger Explanation Reliability Via Model Canonization
* 2023: Optimizing Explanations by Network Canonization and Hyperparameter Search
* 2023: Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations
* 2023: Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
* 2024: Explainable AI for time series via Virtual Inspection Layers
Includes: Lapuschkin, S. Lapuschkin, S.[Sebastian]
12 for Lapuschkin, S.
Lapusta, N.
* 2013: Geostationary Optical Seismometer, Proof of Concept, A
Lapuyade Lahorgue, J.
* 2016: Tumor segmentation by fusion of MRI images using copula based statistical methods
* 2017: Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions
* 2020: Deep learning based automatic detection of uninformative images in pulmonary optical endomicroscopy
Includes: Lapuyade Lahorgue, J. Lapuyade-Lahorgue, J. Lapuyade-Lahorgue, J.[Jérôme]
Lapuyade, J.[Jerome]
* 2009: Three Dimensional Monocular Human Motion Analysis in End-Effector Space