Deep Learning of Behaviors
G. Cybenko (Dartmouth)
- A wide variety of security challenges involve reverse engineering the constituent elements of observed data.
- Data Fission
- Cocktail Party-type problems
- Analysis of Competing Hypotheses
- “Multiple exposure” problem
Key Science Methods & Advances
- Deep Learning has had a huge impact on recognizing individual objects. EG, AlexNet.
- But there has not yet been a similar breakthrough on “disentanglement” problems.
- Combining state-of-the-art recurrent deep neural network learning and deep reinforcement learning, radically new approaches to this problem set are now possible.
Results & Impact
- Street scene video: people walking in different directions, vehicles, animals, bicycles
- Occlusions, formations, different types of kinematics
- Computer network: emails, backups, file shares, remote computing, database accesses
- Encrypted traffic, NATed, partially observable, attacks
- Emails/contacts/browsing/social media: social, job related, hobbies, logistics, health
- Multiplicity of disparate activities, novel activities, partially observable, out-of-band events