23 citations · 48 across the 11 of their papers we have counts for
12 papers
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…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Knowledge-Centric Metacognitive Learning
Arun Kumar, Paul Schrater
Interactions are central to intelligent reasoning and learning abilities, with the interpretation of abstract knowledge guiding meaningful interaction with objects in the environme…
Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control
Morteza Ansarinia, Paul Schrater, Pedro Cardoso-Leite
Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing l…
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…
Belief dynamics extraction
Arun Kumar, Zhengwei Wu, Xaq Pitkow +1
Animal behavior is not driven simply by its current observations, but is strongly influenced by internal states. Estimating the structure of these internal states is crucial for un…
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…
Temporal Proximity induces Attributes Similarity
Arun Kumar, Karan Aggarwal, Paul Schrater
Users consume their favorite content in temporal proximity of consumption bundles according to their preferences and tastes. Thus, the underlying attributes of items implicitly mat…
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
Yash Upadhyay, Paul Schrater
In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of fea…
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-…
Novelty Learning via Collaborative Proximity Filtering
Arun Kumar, Paul Schrater
The vast majority of recommender systems model preferences as static or slowly changing due to observable user experience. However, spontaneous changes in user preferences are ubiq…