Murilo Saraiva de Queiroz

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A novel approach to sentence generation – SegSim, Sentence Generation by Similarity Matching – is outlined, and is argued to possess a number of desirable properties making it plausible as a model of sentence generation in the human brain, and useful as a guide for creating sentence generation components within artificial brains. The crux of the approach is(More)
A software architecture is described which enables a virtual agent in an online virtual world to carry out simple English language interactions grounded in its perceptions and actions. The use of perceptions to guide anaphor resolution is discussed, along with the use of natural language generation to answer simple questions about the observed world. This(More)
In this work, we propose a variation of a direct reinforcement learning algorithm, suitable for usage with spiking neurons based on the spike response model (SRM). The SRM is a biologically inspired, flexible model of spiking neuron based on kernel functions that describe the effect of spike reception and emission on the membrane potential of the neuron. In(More)
Analysis of postgenomic biological data (such as microarray and SNP data) is a subtle art and science, and the statistical methods most commonly utilized sometimes prove inadequate. Machine learning techniques can provide superior understanding in many cases, but are rarely used due to their relative complexity and obscurity. A challenge, then, is to make(More)
The Gene Ontology (GO) database annotates a large number of genes according to their functions (the biological processes, molecular functions and cellular components in which they are involved). However, it is far from complete, and so there is a need for techniques that automatically assign GO functional categories to genes based on integration of(More)
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