Search Results for author:
Found 3 papers, 0 papers with code
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process (POMDP) is known.
Attention as Inference via Fenchel Duality
In particular, we describe a convex optimization problem that arises in a family of estimation tasks commonly appearing in the design of deep learning models.
Multi-step learning and underlying structure in statistical models
We use this to give conditions on the marginal and conditional models which imply an advantage for multi-step learning approaches.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
