4 papers
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
Kevin Vogt-Lowell, Theodoros Tsiligkaridis, Rodney Lafuente-Mercado +4
Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and n…
The Inter-Intra Modal Measure: A Predictive Lens on Fine-Tuning Outcomes in Vision-Language Models
Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis
The fine-tuning of large vision-language foundation models remains an underexplored area, particularly regarding its impact on learning gains and catastrophic forgetting. Inspired…
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP
Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis
Foundations models are presented as generalists that often perform well over a myriad of tasks. Fine-tuning these models, even on limited data, provides an additional boost in task…
Robust Fine-Tuning of Vision-Language Models for Domain Generalization
Kevin Vogt-Lowell, Noah Lee, Theodoros Tsiligkaridis +1
Transfer learning enables the sharing of common knowledge among models for a variety of downstream tasks, but traditional methods suffer in limited training data settings and produ…