6 papers
Completely Positive and Trace Preserving Schemes with Tensor Train Compression for the Lindblad Equation
Peter DelMastro, Daniel Appelö, Yingda Cheng
We propose a family of low-rank, completely positive and trace preserving schemes for the Lindblad equation, a common model for open quantum systems. Low-rank representation is emp…
Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Alexander Yeung, Peter DelMastro, Arjun Karuvally +3
Reservoir Computing is a machine learning approach that uses the rich repertoire of complex system dynamics for function approximation. Current approaches to reservoir computing us…
Transient Dynamics in Lattices of Differentiating Ring Oscillators
Peter DelMastro, Arjun Karuvally, Hananel Hazan +2
Recurrent neural networks (RNNs) are machine learning models widely used for learning temporal relationships. Current state-of-the-art RNNs use integrating or spiking neurons -- tw…
Inexact subspace projection methods for low-rank tensor eigenvalue problems
Alec Dektor, Peter DelMastro, Erika Ye +2
We propose inexact subspace iteration for solving high-dimensional eigenvalue problems with low-rank structure. Inexactness stems from low-rank compression, enabling efficient repr…
Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks
Arjun Karuvally, Peter Delmastro, Hava T. Siegelmann
Understanding the intricate operations of Recurrent Neural Networks (RNNs) mechanistically is pivotal for advancing their capabilities and applications. In this pursuit, we propose…
On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks
Peter DelMastro, Rushiv Arora, Edward Rietman +1
Recurrent Neural Networks (RNNs) have shown great success in modeling time-dependent patterns, but there is limited research on their learned representations of latent temporal fea…