6 accepted papers
While the human brain efficiently handles various computations with a limited number of neurons, traditional deep learning networks require a significant increase in parameters to improve performance. Yet, these parameters are used inefficiently as the networks employ the same amount of computatio…
Reinforcement learning (RL) algorithms have been very successful at tackling complex control problems, such as AlphaGo or fusion control. However, current research mainly emphasizes solution quality, often achieved by using large models trained on large amounts of data, and does not account for the…
Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model…
While the majority of exploratory approaches search for correlations among features of different modalities, indirect/nonlinear relations between structure and function have not yet been fully investigated. In this work, we employ a neural machine translation model [1] to relate two modalities: stru…
Independent vector analysis (IVA) is an approach for joint blind source separation of several data sets that learns simultaneous unmixing transforms for each set. It assumes corresponding sources from different data sets to be statistically dependent. One of the main advantages is IVA's ability to r…
In collaborative medical research settings, a moderate number of groups (sites) may wish to merge local analyses of private subject data. Differential privacy offers one way to guarantee privacy for these local analyses. We describe a novel ensemble learning method that we call the "feature method"…