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Focus On What Matters: Separated Models For Visual-Based RL Generalization
Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.
Soft Superpixel Neighborhood Attention
Images contain objects with deformable boundaries, such as the contours of a human face, yet attention operators act on square windows.
OT4P: UnlockingEffectiveOrthogonalGroupPathfor PermutationRelaxation
This transformation naturally implements a parameterization for the relaxation of permutation matrices, allowing for gradient-based optimization of problems involving permutations.
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain.
Uncovering Safety Risks of Large Language Models through Concept Activation Vector
Warning: This paper contains text examples that are offensive or harmful in nature.
Strategic Linear Contextual Bandits
Recommendation algorithms that select the most relevant item for sequentially arriving users or queries have become vital for navigating the internet and its many online platforms.