135,630 research outputs found
Predicate learning in neural systems:Using oscillations to discover latent structure
Humans learn to represent complex structures (e.g. natural language, music, mathematics) from experience with their environments. Often such structures are latent, hidden, or not encoded in statistics about sensory representations alone. Accounts of human cognition have long emphasized the importance of structured representations, yet the majority of contemporary neural networks do not learn structure from experience. Here, we describe one way that structured, functionally symbolic representations can be instantiated in an artificial neural network. Then, we describe how such latent structures (viz. predicates) can be learned from experience with unstructured data. Our approach exploits two principles from psychology and neuroscience: comparison of representations, and the naturally occurring dynamic properties of distributed computing across neuronal assemblies (viz. neural oscillations). We discuss how the ability to learn predicates from experience, to represent information compositionally, and to extrapolate knowledge to unseen data is core to understanding and modeling the most complex human behaviors (e.g. relational reasoning, analogy, language processing, game play)
Learning through structure: towards deep neuromorphic knowledge graph embeddings
Computing latent representations for graph-structured data is an ubiquitous
learning task in many industrial and academic applications ranging from
molecule synthetization to social network analysis and recommender systems.
Knowledge graphs are among the most popular and widely used data
representations related to the Semantic Web. Next to structuring factual
knowledge in a machine-readable format, knowledge graphs serve as the backbone
of many artificial intelligence applications and allow the ingestion of context
information into various learning algorithms. Graph neural networks attempt to
encode graph structures in low-dimensional vector spaces via a message passing
heuristic between neighboring nodes. Over the recent years, a multitude of
different graph neural network architectures demonstrated ground-breaking
performances in many learning tasks. In this work, we propose a strategy to map
deep graph learning architectures for knowledge graph reasoning to neuromorphic
architectures. Based on the insight that randomly initialized and untrained
(i.e., frozen) graph neural networks are able to preserve local graph
structures, we compose a frozen neural network with shallow knowledge graph
embedding models. We experimentally show that already on conventional computing
hardware, this leads to a significant speedup and memory reduction while
maintaining a competitive performance level. Moreover, we extend the frozen
architecture to spiking neural networks, introducing a novel, event-based and
highly sparse knowledge graph embedding algorithm that is suitable for
implementation in neuromorphic hardware.Comment: Accepted for publication at the International Conference on
Neuromorphic Computing (ICNC 2021
Using deep learning to understand and mitigate the qubit noise environment
Understanding the spectrum of noise acting on a qubit can yield valuable
information about its environment, and crucially underpins the optimization of
dynamical decoupling protocols that can mitigate such noise. However,
extracting accurate noise spectra from typical time-dynamics measurements on
qubits is intractable using standard methods. Here, we propose to address this
challenge using deep learning algorithms, leveraging the remarkable progress
made in the field of image recognition, natural language processing, and more
recently, structured data. We demonstrate a neural network based methodology
that allows for extraction of the noise spectrum associated with any qubit
surrounded by an arbitrary bath, with significantly greater accuracy than the
current methods of choice. The technique requires only a two-pulse echo decay
curve as input data and can further be extended either for constructing
customized optimal dynamical decoupling protocols or for obtaining critical
qubit attributes such as its proximity to the sample surface. Our results can
be applied to a wide range of qubit platforms, and provide a framework for
improving qubit performance with applications not only in quantum computing and
nanoscale sensing but also in material characterization techniques such as
magnetic resonance.Comment: Accepted for publication, 15 pages, 10 figure
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
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