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Effective Parallelisation for Machine Learning
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications.
Predicting Dynamic Difficulty
Motivated by applications in electronic games as well as teaching systems, we investigate the problem of dynamic difficulty adjustment.
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