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We find that larger scale and combined multi-scale representations favor goal-oriented navigation task learning.
This demonstrates long-term retention of the ballistic force task learning.
This would depend upon the details of the task learning and environment adaptation algorithms.
Maastricht University for example, already starts with whole task learning in year 1 [ 29].
Structure 2: Task learning and environment adaptation are performed by a single distinct network for each target.
With this simplification, we could use basis function networks to model task learning rather than other more complicated networks.
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An example on how multi-task learning transfers knowledge between tasks is depicted in Figure 2. Figure 2 Knowledge transfer in multi-task learning.
The proposed algorithm is based on multi-task learning deep neural network that uses a small grayscale image.
Results demonstrate the effectiveness of the proposed multi-task learning formulations for disease progression in comparison with single-task learning algorithms including ridge regression and Lasso.
In Section 2, we present the related work on personality prediction and multi-task learning.
The parameters of these three networks are simultaneously optimized via end-to-end multi-task learning.
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