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Brightness and color constancy is a fundamental problem faced in computer vision and by our own visual system. We easily recognize objects despite changes in illumination, but without a mechanism to cope with this, many object and face recognition systems perform poorly. In this paper we compare approaches in computer vision and computational neuroscience for inducing brightness and color constancy based on their ability to improve recognition. We analyze the relative performance of the algorithms on the AR face and ALOI datasets using both a SIFT-based recognition system and a simple pixel-based approach. Quantitative results demonstrate that color constancy methods can significantly improve classification accuracy. We also evaluate the approaches on the Caltech-101 dataset to determine how these algorithms affect performance under relatively normal illumination conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an account of the acquisition of grammatical relations using the basic concepts of connectionism and a construction-based theory of grammar. Many previous accounts of first-language acquisition assume that grammatical relations (e.g., the grammatical subject and object of a sentence) and linking rules are universal and innate; this is necessary to provide a first set of assumptions in the target language to allow deductive processes to test hypotheses and/or set parameters. In contrast to this approach, we propose that grammatical relations emerge rather late in the language-learning process. Our theoretical proposal is based on two observations. First, early production of childhood speech is formulaic and becomes systematic in a progressive fashion. Second, grammatical relations themselves are family-resemblance categories that cannot be described by a single parameter. This leads to the notion that grammatical relations are learned in a bottom up fashion. Combining this theoretical position with the notion that the main purpose of language is communication, we demonstrate the emergence of the notion of "subject" in a simple recurrent network that learns to map from sentences to semantic roles. We analyze the hidden layer representations of the emergent subject, and demonstrate that these representations correspond to a radially-structured category. We also claim that the pattern of generalization and undergeneralization demonstrated by the network conforms to what we expect from the data on children's generalizations. [ABSTRACT FROM AUTHOR]

At least three disparate channels have been used to install new knowledge into artificial intelligence systems. The first of these is the programmer channel, through which the knowledge in the system is simply edited to include the desired new knowledge. While this method is often effective, it may not be as efficient as learning directly from environmental interaction. The second channel may be called the linguistic channel, through which knowledge is added by explicitly telling the system facts or commands encoded as strings of quasi-linguistic instructions in some appropriate form. Finally, there is, for want of a better phrase, the learning channel, through which the system learns new knowledge in an inductive way via environmental observations and simple feedback information. These latter two channels are the ones upon which we wish to focus, as they are the hallmarks of instructable systems. Most instructable systems depend upon, or at least heavily favor, one of these two channels for the bulk of their knowledge acquisition. Specifically, symbolic artificial intelligence systems have generally depended upon the explicit use of sentential logical expressions, rules, or productions for the transmission of new knowledge to the system. In contrast, many connectionist network models have relied solely on inductive generalization mechanisms for knowledge creation. There is no apparent reason to believe that this rough dichotomy of technique is necessary, however. Systems which are capable of receiving detailed instruction and also generalizing from experience the both possible and potentially very useful. [ABSTRACT FROM AUTHOR]