Visual interpretation of the robustness of Non-Negative Associative Gradient Projection Points over function minimizers in mini-batch sampled loss functions. - Dominic Kafka; Daniel N. Wilke (CoRR, 2019)

Gradient-only line searches: An Alternative to Probabilistic Line Searches. - Dominic Kafka; Daniel N. Wilke (CoRR, 2019)

Investigating the interaction between gradient-only line searches and different activation functions. - Dominic Kafka; Daniel N. Wilke (CoRR, 2020)

Gradient-only line searches to automatically determine learning rates for a variety of stochastic training algorithms. - Dominic Kafka; Daniel Nicolas Wilke (CoRR, 2020)

GOALS: Gradient-Only Approximations for Line Searches Towards Robust and Consistent Training of Deep Neural Networks. - Younghwan Chae; Daniel N. Wilke; Dominic Kafka (CoRR, 2021)

An empirical study into finding optima in stochastic optimization of neural networks. - Dominic Kafka; Daniel N. Wilke (Inf. Sci., 2021)

Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches. - Dominic Kafka; Daniel N. Wilke (J. Glob. Optim., 2021)

Gradient-only surrogate to resolve learning rates for robust and consistent training of deep neural networks. - Younghwan Chae; Daniel N. Wilke; Dominic Kafka (Appl. Intell., 2023)