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of this highly promising modern technology. Deep learning methods are highly effective when targeted and precisely utilized. Incorporating explainable mechanisms will thus strengthen the expertise of biomedical
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, particularly linguistic features derived through sentiment analysis, while simultaneously annotating the dataset with pertinent medical information. Thirdly, the utilization deep learning methodologies, such as
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members of the Topal team have been working on optimizing the training of neural networks by applying techniques from high performance computing, linear and tensor algebra (please, see papers from ICML'23
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, while activation checkpointing recomputes parts of the computational graph when applied, thus saving memory at the price of an overhead on computations. All types of techniques can be combined to achieve
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the intersection between biomedical engineering, complex systems and clinical neuroscience. NERV proposes new computational frameworks to analyze and model the spatiotemporal complexity of brain networks from