20 citations · 48 across the 7 of their papers we have counts for
4 papers · 1 filter
Addressing the Topological Defects of Disentanglement via Distributed Operators
Diane Bouchacourt, Mark Ibrahim, Stéphane Deny
A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in…
Global Explanations of Neural Networks: Mapping the Landscape of Predictions
Mark Ibrahim, Melissa Louie, Ceena Modarres +1
A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduc…
Mixed Membership Recurrent Neural Networks
Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres +2
Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for…
Towards Explainable Deep Learning for Credit Lending: A Case Study
Ceena Modarres, Mark Ibrahim, Melissa Louie +1
Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predic…