Search Results for author: Daniel A. Braun

Found 15 papers, 2 papers with code

Mixture-of-Variational-Experts for Continual Learning

1 code implementation25 Oct 2021 Heinke Hihn, Daniel A. Braun

One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge.

Continual Learning Domain-IL Continual Learning +4

Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts

no code implementations30 Nov 2020 Peter Bellmann, Heinke Hihn, Daniel A. Braun, Friedhelm Schwenker

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).

Classification General Classification +1

Specialization in Hierarchical Learning Systems

no code implementations3 Nov 2020 Heinke Hihn, Daniel A. Braun

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.

Decision Making Density Estimation +1

The Two Kinds of Free Energy and the Bayesian Revolution

1 code implementation24 Apr 2020 Sebastian Gottwald, Daniel A. Braun

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.

Bayesian Inference Decision Making

Hierarchical Expert Networks for Meta-Learning

no code implementations ICML Workshop LifelongML 2020 Heinke Hihn, Daniel A. Braun

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.

Image Classification Meta-Learning +1

An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems

no code implementations26 Jul 2019 Heinke Hihn, Sebastian Gottwald, Daniel A. Braun

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.

Decision Making reinforcement-learning

Bounded rational decision-making from elementary computations that reduce uncertainty

no code implementations8 Apr 2019 Sebastian Gottwald, Daniel A. Braun

In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty.

Decision Making

Systems of bounded rational agents with information-theoretic constraints

no code implementations16 Sep 2018 Sebastian Gottwald, Daniel A. Braun

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

no code implementations4 Sep 2018 Heinke Hihn, Sebastian Gottwald, Daniel A. Braun

Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power.

Decision Making

An information-theoretic on-line update principle for perception-action coupling

no code implementations16 Apr 2018 Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun

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

no code implementations7 Apr 2016 Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun

As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning.

Information-Theoretic Bounded Rationality

no code implementations21 Dec 2015 Pedro A. Ortega, Daniel A. Braun, Justin Dyer, Kee-Eung Kim, Naftali Tishby

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.

Decision Making

Adaptive information-theoretic bounded rational decision-making with parametric priors

no code implementations5 Nov 2015 Jordi Grau-Moya, Daniel A. Braun

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.

Decision Making

Bounded Rational Decision-Making in Changing Environments

no code implementations24 Dec 2013 Jordi Grau-Moya, Daniel A. Braun

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.

Decision Making

Abstraction in decision-makers with limited information processing capabilities

no code implementations16 Dec 2013 Tim Genewein, Daniel A. Braun

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.

Decision Making

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