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Found 8 papers, 5 papers with code
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected dataset.
Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously.
Unsupervised Representation Learning for Time Series: A Review
However, there is a lack of systematic analysis of unsupervised representation learning approaches for time series.
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting.
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations
We then propose DualCF strategy to circumvent the above issues, which is achieved by taking not only CF but also counterfactual explanation of CF (CCF) as pairs of training samples for the substitute model.
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning
As a result, the agent can focus on more informative samples and learn representation invariances more efficiently, with significantly reduced augmented inputs.
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations.
From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition.
