1 code implementation • 16 Apr 2024 • Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut, Tanja Käser
In light of the job market's rapid changes and the current state of research in course recommender systems, we outline essential properties for course recommender systems to address these demands effectively, including explainable, sequential, unsupervised, and aligned with the job market and user's goals.
1 code implementation • 5 Feb 2024 • Vinitra Swamy, Julian Blackwell, Jibril Frej, Martin Jaggi, Tanja Käser
Real-world interpretability for neural networks is a tradeoff between three concerns: 1) it requires humans to trust the explanation approximation (e. g. post-hoc approaches), 2) it compromises the understandability of the explanation (e. g. automatically identified feature masks), and 3) it compromises the model performance (e. g. decision trees).
1 code implementation • 11 Dec 2023 • Jibril Frej, Neel Shah, Marta Knežević, Tanya Nazaretsky, Tanja Käser
In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning.
1 code implementation • 25 Sep 2023 • Vinitra Swamy, Malika Satayeva, Jibril Frej, Thierry Bossy, Thijs Vogels, Martin Jaggi, Tanja Käser, Mary-Anne Hartley
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space.
no code implementations • 1 Jul 2023 • Vinitra Swamy, Jibril Frej, Tanja Käser
Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 2 Dec 2022 • Mohammad Asadi, Vinitra Swamy, Jibril Frej, Julien Vignoud, Mirko Marras, Tanja Käser
Time series is the most prevalent form of input data for educational prediction tasks.
no code implementations • JEPTALNRECITAL 2020 • Hang Le, Lo{\"\i}c Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alex Allauzen, re, Beno{\^\i}t Crabb{\'e}, Laurent Besacier, Didier Schwab
Les mod{\`e}les de langue pr{\'e}-entra{\^\i}n{\'e}s sont d{\'e}sormais indispensables pour obtenir des r{\'e}sultats {\`a} l{'}{\'e}tat-de-l{'}art dans de nombreuses t{\^a}ches du TALN.
no code implementations • 24 Apr 2020 • Jibril Frej, Phillipe Mulhem, Didier Schwab, Jean-Pierre Chevallet
Document indexing is a key component for efficient information retrieval (IR).
7 code implementations • LREC 2020 • Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Ranked #1 on Natural Language Inference on XNLI French
1 code implementation • LREC 2020 • Jibril Frej, Didier Schwab, Jean-Pierre Chevallet
Since most standard ad-hoc information retrieval datasets publicly available for academic research (e. g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets.