15 citations · 22 across the 2 of their papers we have counts for
9 papers
Nondeterminism and Instability in Neural Network Optimization
Cecilia Summers, Michael J. Dinneen
Nondeterminism in neural network optimization produces uncertainty in performance, making small improvements difficult to discern from run-to-run variability. While uncertainty can…
Improved Adversarial Robustness via Logit Regularization Methods
Cecilia Summers, Michael J. Dinneen
While great progress has been made at making neural networks effective across a wide range of visual tasks, most models are surprisingly vulnerable. This frailness takes the form o…
Four Things Everyone Should Know to Improve Batch Normalization
Cecilia Summers, Michael J. Dinneen
A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep ar…
A Hybrid Quantum-Classical Paradigm to Mitigate Embedding Costs in Quantum Annealing---Abridged Version
Alastair A. Abbott, Cristian S. Calude, Michael J. Dinneen +1
Quantum annealing has shown significant potential as an approach to near-term quantum computing. Despite promising progress towards obtaining a quantum speedup, quantum annealers a…
Experimentally Probing the Algorithmic Randomness and Incomputability of Quantum Randomness
Alastair A. Abbott, Cristian S. Calude, Michael J. Dinneen +1
The advantages of quantum random number generators (QRNGs) over pseudo-random number generators (PRNGs) are normally attributed to the nature of quantum measurements. This is often…
Improved Mixed-Example Data Augmentation
Cecilia Summers, Michael J. Dinneen
In order to reduce overfitting, neural networks are typically trained with data augmentation, the practice of artificially generating additional training data via label-preserving…