61 citations · 144 across the 21 of their papers we have counts for
4 papers · 1 filter
The smooth output assumption, and why deep networks are better than wide ones
Luis Sa-Couto, Jose Miguel Ramos, Andreas Wichert
When several models have similar training scores, classical model selection heuristics follow Occam's razor and advise choosing the ones with least capacity. Yet, modern practice w…
Understanding the double descent curve in Machine Learning
Luis Sa-Couto, Jose Miguel Ramos, Miguel Almeida +1
The theory of bias-variance used to serve as a guide for model selection when applying Machine Learning algorithms. However, modern practice has shown success with over-parameteriz…
Multi-level Data Representation For Training Deep Helmholtz Machines
Jose Miguel Ramos, Luis Sa-Couto, Andreas Wichert
A vast majority of the current research in the field of Machine Learning is done using algorithms with strong arguments pointing to their biological implausibility such as Backprop…
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
Catarina Moreira, Renuka Sindhgatta, Chun Ouyang +2
This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. W…