Human visual perception carves a scene at its physical joints, decomposing the world into
objects, which are selectively attended, tracked and predicted as we engage our …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Dense Associative Memories or Modern Hopfield Networks have many appealing properties
of associative memory. They can do pattern completion, store a large number of memories …

Although deep feedforward neural networks share some characteristics with the primate
visual system, a key distinction is their dynamics. Deep nets typically operate in serial stages …

The present study analyzes the retrieval capacity of an Ensemble of diluted Attractor Neural
Networks for real patterns (ie, non-random ones), as it is the case of human fingerprints. We …

In this work, we explore the use of Tensor Product Representations (TPRs) in a Vision
Transformer model to form image representations that can later be used for symbolic …

Deep neural networks (NNs) recently revolutionized the field of Artificial Intelligence, making
great progress in computer vision, natural language processing, complex game play …

Deep neural networks learn representations of data to facilitate problem-solving in their
respective domains. However, they struggle to acquire a structured representation based on …

Artificial neural networks have become ubiquitous for machine perception, yielding
unprecedented performance across an ever-increasing range of tasks and domains …

By decomposing the world in terms of objects, humans are able to recombine their existing
knowledge in a virtually unbounded number ways to understand unfamiliar situations, make …