Search Results for author:
Found 17 papers, 3 papers with code
Automatic Bat Call Classification using Transformer Networks
Major challenges in automatic bat call identification are high call variability, similarities between species, interfering calls and lack of annotated data.
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.
The Two Kinds of Free Energy and the Bayesian Revolution
The concept of free energy has its origins in 19th century thermodynamics, but has recently found its way into the behavioral and neural sciences, where it has been promoted for its wide applicability and has even been suggested as a fundamental principle of understanding intelligent behavior and brain function.
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 from elementary computations that reduce uncertainty
In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty.
Systems of bounded rational agents with information-theoretic constraints
Specialization and hierarchical organization are important features of efficient collaboration in economical, artificial, and biological systems.
Bounded Rational Decision-Making with Adaptive Neural Network Priors
Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power.
An information-theoretic on-line update principle for perception-action coupling
Here we consider perception and action as two serial information channels with limited information-processing capacity.
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning.
Information-Theoretic Bounded Rationality
Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics.
Adaptive information-theoretic bounded rational decision-making with parametric priors
Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory.
Bounded Rational Decision-Making in Changing Environments
When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past.
Abstraction in decision-makers with limited information processing capabilities
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise.
