Search Results for author:
Found 5 papers, 0 papers with code
Having Second Thoughts? Let's hear it
Deep learning models loosely mimic bottom-up signal pathways from low-order sensory areas to high-order cognitive areas.
DL-based prediction of optimal actions of human experts
Once developed properly, expert systems can assist or substitute human experts, but they require overly expensive knowledge engineering/acquisition.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data.
DynMat, a network that can learn after learning
To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience.
