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Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning
Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards.
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning
Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources.
Learning Geometry-aware Representations by Sketching
Our method, coined Learning by Sketching (LBS), learns to convert an image into a set of colored strokes that explicitly incorporate the geometric information of the scene in a single inference step without requiring a sketch dataset.
DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning
In Self-Supervised Learning (SSL), it is known that frequent occurrences of the collision in which target data and its negative samples share the same class can decrease performance.
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data.
