123 citations · 145 across the 17 of their papers we have counts for
7 papers · 1 filter
A Human-Centric Approach to Explainable AI for Personalized Education
Vinitra Swamy
Deep neural networks form the backbone of artificial intelligence research, with potential to transform the human experience in areas ranging from autonomous driving to personal as…
Intrinsic User-Centric Interpretability through Global Mixture of Experts
Vinitra Swamy, Syrielle Montariol, Julian Blackwell +3
In human-centric settings like education or healthcare, model accuracy and model explainability are key factors for user adoption. Towards these two goals, intrinsically interpreta…
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Vinitra Swamy, Malika Satayeva, Jibril Frej +5
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potenti…
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
Vinitra Swamy, Jibril Frej, Tanja Käser
Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems. As models get larger, more ubiquitous, and pervas…
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Mohammad Asadi, Vinitra Swamy, Jibril Frej +3
Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, design…
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
Vinitra Swamy, Bahar Radmehr, Natasa Krco +2
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainab…
Interpreting Language Models Through Knowledge Graph Extraction
Vinitra Swamy, Angelika Romanou, Martin Jaggi
Transformer-based language models trained on large text corpora have enjoyed immense popularity in the natural language processing community and are commonly used as a starting poi…