BRAINet presents a new approach, different than the methods commonly used today, to automatically learn the structure of neural architectures. This theoretically grounded approach learns qualities of the training data in an unsupervised manner, and enables building compact and sparse structures, which can be used to achieve various goals on that data. In addition, it models uncertainty inherently, and is computationally efficient, converging within a few hours on a single desktop CPU. We believe that the resulting models can also be effective at detecting adversarial attacks, where an adversarial attack can be interpreted as an intervention on the input data. We plan to explore this in our future work.
For more details please see our recent papers and follow us on @IntelAIResearch for updates on our work.
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