7 papers
Multimodal Taxonomic Conditioning for Generative Plankton Imagery
Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault
Automated plankton imaging produces severely long-tailed datasets, where the rare taxa of greatest ecological interest have too few images to train or evaluate classifiers reliably…
Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
Ozgu Goksu, Nicolas Pugeault
Federated Learning (FL) enables decentralised model training across distributed clients without requiring data centralisation. However, the generalisation performance of the global…
GIFT: Global stabilisation via Intrinsic Fine Tuning
Rory Young, Nicolas Pugeault
Deep reinforcement learning policies achieve strong performance in complex continuous control environments with nonlinear contact forces. However, these policies often produce chao…
FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity
Ozgu Goksu, Nicolas Pugeault
Federated Learning (FL) provides decentralised model training, which effectively tackles problems such as distributed data and privacy preservation. However, the generalisation of…
Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift
Ozgu Goksu, Nicolas Pugeault
Federated Learning offers a solution for decentralised model training, addressing the difficulties associated with distributed data and privacy in machine learning. However, the fa…
Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
Rory Young, Nicolas Pugeault
Deep reinforcement learning agents achieve state-of-the-art performance in a wide range of simulated control tasks. However, successful applications to real-world problems remain l…
The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation
Ozgu Goksu, Nicolas Pugeault
The pursuit of learning robust representations without human supervision is a longstanding challenge. The recent advancements in self-supervised contrastive learning approaches hav…