7 papers
Distance by de-correlation: Computing distance with heterogeneous grid cells
Pritipriya Dasbehera, Akshunna S. Dogra, William T. Redman
Encoding the distance between locations in space is essential for accurate navigation. Grid cells, a functional class of neurons in medial entorhinal cortex, are believed to suppor…
Shortcut Solutions Learned by Transformers Impair Continual Compositional Reasoning
William T. Redman, Erik C. Johnson, Brian Robinson
Identifying and exploiting common features across domains is at the heart of the human ability to make analogies, and is believed to be crucial for the ability to continually learn…
Interpreting Reinforcement Learning Model Behavior via Koopman with Control
William T. Redman
Reinforcement learning (RL) models have shown the capability of learning complex behaviors, but quantitatively assessing those behaviors - which is critical for safety assurance an…
Trojans in Artificial Intelligence (TrojAI) Final Report
Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68
The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…
On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks
William T. Redman, Zhangyang Wang, Alessandro Ingrosso +1
Since its use in the Lottery Ticket Hypothesis, iterative magnitude pruning (IMP) has become a popular method for extracting sparse subnetworks that can be trained to high performa…
Koopman Learning with Episodic Memory
William T. Redman, Dean Huang, Maria Fonoberova +1
Koopman operator theory has found significant success in learning models of complex, real-world dynamical systems, enabling prediction and control. The greater interpretability and…