23 citations · 48 across the 11 of their papers we have counts for
5 papers · 1 filter
Structured Relational Representations
Arun Kumar, Paul Schrater
Invariant representations are core to representation learning, yet a central challenge remains: uncovering invariants that are stable and transferable without suppressing task-rele…
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding
Thomas Gebhart, Jakob Hansen, Paul Schrater
Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting represent…
Characterizing the Shape of Activation Space in Deep Neural Networks
Thomas Gebhart, Paul Schrater, Alan Hylton
The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer struc…
Inverse Rational Control: Inferring What You Think from How You Forage
Zhengwei Wu, Paul Schrater, Xaq Pitkow
Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning. Inferring an agent's internal model is a…
Adversary Detection in Neural Networks via Persistent Homology
Thomas Gebhart, Paul Schrater
We outline a detection method for adversarial inputs to deep neural networks. By viewing neural network computations as graphs upon which information flows from input space to out-…