Search Results for author:
Found 16 papers, 0 papers with code
Competitive learning to generate sparse representations for associative memory
Yet, associative memory can only work with logarithmic sparse representations, which makes it extremely difficult to apply the model to real data.
The smooth output assumption, and why deep networks are better than wide ones
We call it the output sharpness, and it is based on the fact that, in reality, boundaries between concepts are generally unsharp.
Understanding the double descent curve in Machine Learning
The theory of bias-variance used to serve as a guide for model selection when applying Machine Learning algorithms.
Multi-level Data Representation For Training Deep Helmholtz Machines
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 Backpropagation, deviating the field's focus from understanding its original organic inspiration to a compulsive search for optimal performance.
Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?
In tasks for which there is a vast amount of labeled data, Deep Networks seem to solve this issue with many layers and a non-Hebbian backpropagation algorithm.
Classification and Generation of real-world data with an Associative Memory Model
Using our recently proposed sparse coding prescription for visual patterns, this model can store and retrieve an impressive amount of real-world data in a fault-tolerant manner.
Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition
For these reasons it is still a key research question to look into computational principles in the brain that can help guide models to unsupervisedly learn good representations which can then be used to perform tasks like classification.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
Decision Making
Interpretability Techniques for Deep Learning
Towards a Quantum-Like Cognitive Architecture for Decision-Making
We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models.
Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle
The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action.
The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty
In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands.
The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events.
On Projection Based Operators in Lp space for Exact Similarity Search
We investigate exact indexing for high dimensional Lp norms based on the 1-Lipschitz property and projection operators.
A Quantum Production Model
The production system is a theoretical model of computation relevant to the artificial intelligence field allowing for problem solving procedures such as hierarchical tree search.
Interference Effects in Quantum Belief Networks
This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making.
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list.
