Search Results for author:
Found 10 papers, 6 papers with code
Course Recommender Systems Need to Consider the Job Market
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.
InterpretCC: Conditional Computation for Inherently Interpretable Neural Networks
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).
Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning.
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space.
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
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)
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Time series is the most prevalent form of input data for educational prediction tasks.
FlauBERT : des mod\`eles de langue contextualis\'es pr\'e-entra\^\in\'es pour le fran\ccais (FlauBERT : Unsupervised Language Model Pre-training for French)
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.
Learning Term Discrimination
Document indexing is a key component for efficient information retrieval (IR).
FlauBERT: Unsupervised Language Model Pre-training for French
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
WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset
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.
