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Found 7 papers, 3 papers with code
Explaining Text Classifiers with Counterfactual Representations
One well motivated explanation method for classifiers leverages counterfactuals which are hypothetical events identical to real observations in all aspects except for one categorical feature.
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners.
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents
We introduce a new dataset named WikiVitals which contains a large graph of 48k mutually referred Wikipedia articles classified into 32 categories and connected by 2. 3M edges.
Reconciling Causality and Statistics
Statisticians have warned us since the early days of their discipline that experimental correlation between two observations by no means implies the existence of a causal relation.
Deep Learning Models for Automatic Summarization
Text summarization is an NLP task which aims to convert a textual document into a shorter one while keeping as much meaning as possible.
A Primer on Domain Adaptation
Standard supervised machine learning assumes that the distribution of the source samples used to train an algorithm is the same as the one of the target samples on which it is supposed to make predictions.
On Generalization and Regularization in Deep Learning
Why do large neural network generalize so well on complex tasks such as image classification or speech recognition?
