334 research outputs found
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Fewer epistemological challenges for connectionism
Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolenskyâs paper 'On the proper treatment of connectionism'. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field
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Neural-Symbolic Learning and Reasoning: Contributions and Challenges
The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar
Modeling Relational Data with Graph Convolutional Networks
Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
remain incomplete. We introduce Relational Graph Convolutional Networks
(R-GCNs) and apply them to two standard knowledge base completion tasks: Link
prediction (recovery of missing facts, i.e. subject-predicate-object triples)
and entity classification (recovery of missing entity attributes). R-GCNs are
related to a recent class of neural networks operating on graphs, and are
developed specifically to deal with the highly multi-relational data
characteristic of realistic knowledge bases. We demonstrate the effectiveness
of R-GCNs as a stand-alone model for entity classification. We further show
that factorization models for link prediction such as DistMult can be
significantly improved by enriching them with an encoder model to accumulate
evidence over multiple inference steps in the relational graph, demonstrating a
large improvement of 29.8% on FB15k-237 over a decoder-only baseline
Fast, Linear Time Hierarchical Clustering using the Baire Metric
The Baire metric induces an ultrametric on a dataset and is of linear
computational complexity, contrasted with the standard quadratic time
agglomerative hierarchical clustering algorithm. In this work we evaluate
empirically this new approach to hierarchical clustering. We compare
hierarchical clustering based on the Baire metric with (i) agglomerative
hierarchical clustering, in terms of algorithm properties; (ii) generalized
ultrametrics, in terms of definition; and (iii) fast clustering through k-means
partititioning, in terms of quality of results. For the latter, we carry out an
in depth astronomical study. We apply the Baire distance to spectrometric and
photometric redshifts from the Sloan Digital Sky Survey using, in this work,
about half a million astronomical objects. We want to know how well the (more
costly to determine) spectrometric redshifts can predict the (more easily
obtained) photometric redshifts, i.e. we seek to regress the spectrometric on
the photometric redshifts, and we use clusterwise regression for this.Comment: 27 pages, 6 tables, 10 figure
Diagnostic classification of childhood cancer using multiscale transcriptomics
The causes of pediatric cancersâ distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types
Cultural and Media Identity Among Latvian Migrants in Germany
This chapter explores how transnational media and culture impacts on the identity formation of recent Latvian migrants in Germany. In the context of the EU, Germany opened its labour market to the new EU countries rather late, when compared to other âoldâ EU countries. This has had an effect on the composition of the group of Latvian migrants going to Germany, and their identities. In the light of this, this chapter examines how Latvian migrants in Germany feel and experience their belonging to Latvia and its culture. It analyses the social and communicative practices crucial for the development of belonging, including the rootedness in the country where they live and the cultural references that are important for them. The evidence for the analysis in this chapter comes from in-depth interviews, open media diaries and network maps of Latvian migrants in Germany. The chapter situates the description of evidence in the framework of cultural identity concepts and discusses the role of culture and media in the process of building migrant identity. The chapter argues that culture is shaping the transnational self-perception of Latvian migrants in Germany â as it provides collective narratives of imagined common frames of references, and confirms feelings of belonging and distinction
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