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One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechani ...
Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...
This master thesis provides in-depth explanations of how deep learning and graph theory can be used together to perform pointwise classification in 3D point clouds obtained by combinations of geospatial images. That scene understanding problem arises in a ...
The design and analysis of machine learning algorithms typically considers the problem of learning on a single task, and the nature of learning in such scenario is well explored. On the other hand, very often tasks faced by machine learning systems arrive ...
The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedfo ...
Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...
Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...
In this paper, we examine the learning mechanism of adaptive agents over weakly-connected graphs and reveal an interesting behavior on how information flows through such topologies. The results clarify how asymmetries in the exchange of data can mask local ...
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
We report on \emph{Krum}, the first \emph{provably} Byzantine-tolerant aggregation rule for distributed Stochastic Gradient Descent (SGD). Krum guarantees the convergence of SGD even in a distributed setting where (asymptotically) up to half of the workers ...
