109,947 research outputs found
Neuro-symbolic computing with spiking neural networks
Knowledge graphs are an expressive and widely used data structure due to
their ability to integrate data from different domains in a sensible and
machine-readable way. Thus, they can be used to model a variety of systems such
as molecules and social networks. However, it still remains an open question
how symbolic reasoning could be realized in spiking systems and, therefore, how
spiking neural networks could be applied to such graph data. Here, we extend
previous work on spike-based graph algorithms by demonstrating how symbolic and
multi-relational information can be encoded using spiking neurons, allowing
reasoning over symbolic structures like knowledge graphs with spiking neural
networks. The introduced framework is enabled by combining the graph embedding
paradigm and the recent progress in training spiking neural networks using
error backpropagation. The presented methods are applicable to a variety of
spiking neuron models and can be trained end-to-end in combination with other
differentiable network architectures, which we demonstrate by implementing a
spiking relational graph neural network.Comment: Accepted for publication at the International Conference on
Neuromorphic Systems (ICONS) 202
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
Despite rapid progress in Visual question answering (VQA), existing datasets
and models mainly focus on testing reasoning in 2D. However, it is important
that VQA models also understand the 3D structure of visual scenes, for example
to support tasks like navigation or manipulation. This includes an
understanding of the 3D object pose, their parts and occlusions. In this work,
we introduce the task of 3D-aware VQA, which focuses on challenging questions
that require a compositional reasoning over the 3D structure of visual scenes.
We address 3D-aware VQA from both the dataset and the model perspective. First,
we introduce Super-CLEVR-3D, a compositional reasoning dataset that contains
questions about object parts, their 3D poses, and occlusions. Second, we
propose PO3D-VQA, a 3D-aware VQA model that marries two powerful ideas:
probabilistic neural symbolic program execution for reasoning and deep neural
networks with 3D generative representations of objects for robust visual
recognition. Our experimental results show our model PO3D-VQA outperforms
existing methods significantly, but we still observe a significant performance
gap compared to 2D VQA benchmarks, indicating that 3D-aware VQA remains an
important open research area.Comment: Accepted by NeurIPS202
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
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