8 citations · 9 across the 7 of their papers we have counts for
7 papers
Competitive learning to generate sparse representations for associative memory
Luis Sacouto, Andreas Wichert
One of the most well established brain principles, hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spaw…
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…
Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?
Maria Osório, Luís Sa-Couto, Andreas Wichert
It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect…
Classification and Generation of real-world data with an Associative Memory Model
Rodrigo Simas, Luis Sa-Couto, Andreas Wichert
Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biologica…
Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition
Luis Sa-Couto, Andreas Wichert
Although deep learning has solved difficult problems in visual pattern recognition, it is mostly successful in tasks where there are lots of labeled training data available. Furthe…