7 citations · 9 across the 11 of their papers we have counts for
6 papers · 1 filter
High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Masih Aminbeidokhti, Heitor Rapela Medeiros, Srikanth Muralidharan +2
Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial compu…
Robust and Efficient Finetuning of Vision Foundation Models via Implicit Ensembling
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger +1
Robust finetuning of foundation models seeks to improve out-of-distribution (OOD) generalization during in-distribution (ID) adaptation. We revisit Mixout, a stochastic regularizer…
Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting
Heitor R. Medeiros, Hossein Sharifi-Noghabi, Gabriel L. Oliveira +1
Real-world time series often exhibit a non-stationary nature, degrading the performance of pre-trained forecasting models. Test-Time Adaptation (TTA) addresses this by adjusting mo…
Domain Generalization by Rejecting Extreme Augmentations
Masih Aminbeidokhti, Fidel A. Guerrero Peña, Heitor Rapela Medeiros +3
Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. Howev…
Deep Categorization with Semi-Supervised Self-Organizing Maps
Pedro H. M. Braga, Heitor R. Medeiros, Hansenclever F. Bassani
Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Label…
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
Hansenclever F. Bassani, Renie A. Delgado, Jose Nilton de O. Lima Junior +3
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in t…