Sebastian Lapuschkin
12 papers · 2016–2026 · 7 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (23) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Half 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🏆
Grand Slam
🧬
Topic Evolution
🧭
Keyword Pioneer
🤝
Dynamic Duo
(11)
🗃️
Keyword Collector
(63)
💎
Century Club · Bronze
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The Questioner
Top co-authors
Research topics
Artificial Intelligence
(9)
Machine Learning
(9)
Core AI
(9)
Deep Learning
(8)
Core Methods
(6)
Architectures
(3)
Learning Types
(3)
Optimization & Theory · Machine Learning
(3)
Optimization & Theory · Deep Learning
(3)
Application Areas
(2)
Techniques
(2)
Computer Vision
(1)
Healthcare & Medicine
(1)
Mathematics & Optimization
(1)
Clinical
(1)
Keywords
neural network
(5)
latent representation
(4)
layer-wise relevance propagation
(3)
concept learning
(2)
deep neural network
(2)
explainable ai
(2)
generative model
(2)
image classification
(2)
concept activation vector
(2)
explanation evaluation
(2)
transformer
(1)
efficient computing
(1)
interpretability evaluation
(1)
score matching
(1)
medical imaging
(1)
adversarial learning
(1)
model adaptation
(1)
bias mitigation
(1)
feature attribution
(1)
feature extraction
(1)
Papers
From Weights to Activations: Is Steering the Next Frontier of Adaptation?
ACL 2026
FADE: Why Bad Descriptions Happen to Good Features
ACL 2025
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
ICLR 2025
AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers
ICML 2024
CoSy: Evaluating Textual Explanations of Neurons
NeurIPS 2024
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space
AAAI 2024
Generative Fractional Diffusion Models
NeurIPS 2024
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
JMLR 2023
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
CVPR 2023
iNNvestigate Neural Networks!
JMLR 2019
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
CVPR 2016
The LRP Toolbox for Artificial Neural Networks
JMLR 2016