11 citations · 12 across the 4 of their papers we have counts for
4 papers
Bandit-Driven Batch Selection for Robust Learning under Label Noise
Michal Lisicki, Mihai Nica, Graham W. Taylor
We introduce a novel approach for batch selection in Stochastic Gradient Descent (SGD) training, leveraging combinatorial bandit algorithms. Our methodology focuses on optimizing t…
Empirical analysis of representation learning and exploration in neural kernel bandits
Michal Lisicki, Arash Afkanpour, Graham W. Taylor
Neural bandits have been shown to provide an efficient solution to practical sequential decision tasks that have nonlinear reward functions. The main contributor to that success is…
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization
Michal Lisicki, Arash Afkanpour, Graham W. Taylor
Neural combinatorial optimization (NCO) aims at designing problem-independent and efficient neural network-based strategies for solving combinatorial problems. The field recently e…
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
Dhanesh Ramachandram, Michal Lisicki, Timothy J. Shields +2
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images. Deep…