229 citations · 274 across the 13 of their papers we have counts for
13 papers
Man, Machine, and Mathematics
Akshunna S. Dogra
Nonlinear models and optimization methods have successfully tackled a rapidly growing set of problems in recent years. Indeed, a relatively small toolbox of such models and methods…
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…
FINDER: Feature Inference on Noisy Datasets using Eigenspace Residuals
Trajan Murphy, Akshunna S. Dogra, Hanfeng Gu +3
''Noisy'' datasets (regimes with low signal to noise ratios, small sample sizes, faulty data collection, etc) remain a key research frontier for classification methods with both th…
SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir +2
The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructu…
Universality of Winning Tickets: A Renormalization Group Perspective
William T. Redman, Tianlong Chen, Zhangyang Wang +1
Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possib…
Local error quantification for Neural Network Differential Equation solvers
Akshunna S. Dogra, William T Redman
Neural networks have been identified as powerful tools for the study of complex systems. A noteworthy example is the neural network differential equation (NN DE) solver, which can…
Error Estimation and Correction from within Neural Network Differential Equation Solvers
Akshunna S. Dogra
Neural Network Differential Equation (NN DE) solvers have surged in popularity due to a combination of factors: computational advances making their optimization more tractable, the…
Optimizing Neural Networks via Koopman Operator Theory
Akshunna S. Dogra, William T Redman
Koopman operator theory, a powerful framework for discovering the underlying dynamics of nonlinear dynamical systems, was recently shown to be intimately connected with neural netw…
Dynamical Systems and Neural Networks
Akshunna S. Dogra
Neural Networks (NNs) have been identified as a potentially powerful tool in the study of complex dynamical systems. A good example is the NN differential equation (DE) solver, whi…
Impact of ionizing radiation on superconducting qubit coherence
Antti Vepsäläinen, Amir H. Karamlou, John L. Orrell +11
The practical viability of any qubit technology stands on long coherence times and high-fidelity operations, with the superconducting qubit modality being a leading example. Howeve…
Optimal Presentations of Mathematical Objects
Akshunna Shaurya Dogra
We discuss the optimal presentations of mathematical objects under well defined symbol libraries. We shall examine what light our chosen symbol libraries and syntax shed upon the o…
Geometry of transient chaos in streamwise-localized pipe flow turbulence
Nazmi Burak Budanur, Akshunna S. Dogra, Björn Hof
In pipes and channels, the onset of turbulence is initially dominated by localized transients, which lead to sustained turbulence through their collective dynamics. In the present…
Minimal Representations of Natural Numbers Under a Set of Operators
Akshunna Shaurya Dogra
This paper studies the minimal length representation of the natural numbers. Let O be a fixed set of integer-valued functions (primarily hyperoperations). For each n, what is the s…