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Found 7 papers, 2 papers with code
Hierarchically Structured Task-Agnostic Continual Learning
Due to the general formulation based on generic utility functions, we can apply this optimality principle to a large variety of learning problems, including supervised learning, reinforcement learning, and generative modeling.
Mixture-of-Variational-Experts for Continual Learning
One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge.
Ranked #1 on
Domain-IL Continual Learning
on Cifar10 (5 tasks)
Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts
In the current study, we focus on binary, imbalanced classification tasks, i. e.~binary classification tasks in which one of the two classes is under-represented (minority class) in comparison to the other class (majority class).
Specialization in Hierarchical Learning Systems
Joining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and specialization.
Hierarchical Expert Networks for Meta-Learning
The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations.
An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems
We demonstrate the approach for decision-making problems whose complexity exceeds the capabilities of individual decision-makers, but can be solved by combining the decision-makers optimally.
Bounded Rational Decision-Making with Adaptive Neural Network Priors
Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power.
