Caruana, R. (1993). Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, San Mateo, CA.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... of network familiarity with the input (Baluja and Pomerleau, 1995) A variant of reconstruction constraints was used in regression probelms when some of the inputs were also used as network outputs (Caruana and de Sa, 1997) They suggested a multi task learning (MTL) approach for training networks (Caruana, 1993), however, their approach did not include a regularization between the di#erent task, which we find essential. Our motivation for using reconstruction as a constraint to classification is the simple fact that this is an essential taks of (visual) cortex, and thus, it may be possible that ....

Caruana, R. (1993). Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, San Mateo, CA.

.... of network familiarity with the input (Baluja and Pomerleau, 1995) A variant of reconstruction constraints was used in regression problems when some of the inputs were also used as network outputs (Caruana and de Sa, 1997) They suggested a multi task learning (MTL) approach for training networks (Caruana, 1993), however, their approach did not include a regularization between the different task, which we find essential. Our motivation for using reconstruction as a constraint to classification is the simple fact that this is an essential task of (visual) cortex, and thus, it may be possible that ....

Caruana, R. (1993). Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, San Mateo, CA.

....separaten Lernl aufen f ur jede Zielvariable einen Klassifikator zu induzieren. Dabei werden die m oglichen Gemeinsamkeiten, die die Zielvariablen und ihre Klassifikatoren aufweisen k onnten, nicht ausgenutzt. Beim sogenannten Mehraufgaben Transfer Lernen (MTL, engl. Multi Task Learning) das von Caruana (1993) zum ersten Mal vorgeschlagen wurde, versucht man daher, aus dem Lernergebnis f ur eine Zielvariable diejenigen Bestandteile, die f ur das Lernen einer anderen Zielvariable hilfreich sein k onnten, zu extrahieren und in geeigneter Form in das Lernen dieser anderen Zielvariablen zu transferieren. ....

Caruana, Richard A. (1993). Multitask connectionist learning.

....learning machine s finding a suboptimal representation space, are easily addressed by our algorithm, as discussed in section 4. It has been observed in the past that training a classifier on multiple tasks (using the same data) may be an efficient way to introduce desirable bias into the solution (Caruana, 1993). Our motivation for multiple task training is, however, fundamentally different from the subsequent development of that idea by Caruana (1995) who implicitly assumes that the different tasks are on the same level of categorization. In comparison, our approach calls for internal representation to ....

Caruana, R. (1993). Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, San Mateo, CA.

....Supported by the Deutsche Forschungsgemeinschaft (DFG) Grant Am 60 9 1. 1 Introduction There is empirical evidence that sometimes performance of learning systems improves when they are modified to learn auxiliary, related tasks (called contexts) in addition to the primary tasks of interest [7, 8, 28]. For example, an experimental system to predict the value of German Daimler stock performed better when it was modified to track simultaneously the German stock index DAX [4] The value of the Daimler stock here is the primary or target concept and the value of the DAX a related ....

R. A. Caruana. Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, 1993.

....between the intended target tasks and useful associated contexts. 1 Introduction There is empirical evidence that in many cases performance of learning systems improves when they are modified to learn auxiliary, related tasks (called contexts) in addition to the primary tasks of interest [6, 7, 19]. For example, an experimental system to predict the value of German Daimler stock performed better when it was modified to track simultaneously the German stock index DAX [2] The value of the Daimler stock here is the primary or target concept and the value of the DAX a related ....

R. A. Caruana. Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, 1993.

....learning machine s finding a suboptimal representation space, are easily addressed by our algorithm, as discussed in section 4. It has been observed in the past that training a classifier on multiple tasks (using the same data) may be an efficient way to introduce desirable bias into the solution (Caruana, 1993). Our motivation for multiple task training is, however, fundamentaly different from the subsequent development of that idea by Caruana (1995) who implicitly assumes that the different tasks are on the same level of categorization (as does Baxter, 1995) In comparison, our approach calls for ....

Caruana, R. (1993). Multitask connectionist learning. In Proceedings of the 1993 Connectionist Models Summer School, pages 372--379, San Mateo, CA.

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R. Caruana, "Multitask Connectionist Learning," Proceedings of the 1993 Connectionist Models Summer School, pp. 372--379, 1993.

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