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.
- Constructing Deep Neural Networks by Bayesian Network Structure Learning (NeurIPS 2018)
- Bayesian Structure Learning by Recursive Bootstrap (NeurIPS 2018)
- Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections (NeurIPS 2019)
“Hear more about our technologies, sign up here ”Back to homepage
Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com
Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© Intel Corporation.