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Found 7 papers, 1 papers with code
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.
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.
On the Importance of Critical Period in Multi-stage Reinforcement Learning
The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity.
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents
To validate this hypothesis, we adapt this notion of critical periods to learning in AI agents and investigate the critical period in the virtual environment for AI agents.
Learning task-agnostic representation via toddler-inspired learning
One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e. g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own.
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
