Search Results for author:
Found 14 papers, 4 papers with code
KIX: A Metacognitive Generalization Framework
Humans and other animals aptly exhibit general intelligence behaviors in solving a variety of tasks with flexibility and ability to adapt to novel situations by reusing and applying high level knowledge acquired over time.
Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control
This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding
Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting representations encode the known factual information represented by the knowledge graph and can be used in the inference of new relations.
A Unified Paths Perspective for Pruning at Initialization
A number of recent approaches have been proposed for pruning neural network parameters at initialization with the goal of reducing the size and computational burden of models while minimally affecting their training dynamics and generalization performance.
The structure of behavioral data
For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results.
Neuromatch Academy: Teaching Computational Neuroscience with global accessibility
Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects.
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
This problem can be solved by control theory, which allows us to find the optimal actions for a given system dynamics and objective function.
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
This approach provides a foundation for interpreting the behavioral and neural dynamics of highly adapted controllers in animal brains.
Belief dynamics extraction
Estimating the structure of these internal states is crucial for understanding the neural basis of behavior.
Characterizing the Shape of Activation Space in Deep Neural Networks
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 structure.
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis.
Inverse Rational Control: Inferring What You Think from How You Forage
Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning.
Adversary Detection in Neural Networks via Persistent Homology
We outline a detection method for adversarial inputs to deep neural networks.
Novelty Learning via Collaborative Proximity Filtering
We meet these challenges by developing a model of novelty preferences that learns and tracks latent user tastes.
