no code implementations • 20 Sep 2023 • Frank Fundel, Daniel A. Braun, Sebastian Gottwald
Major challenges in automatic bat call identification are high call variability, similarities between species, interfering calls and lack of annotated data.
1 code implementation • 14 Nov 2022 • Heinke Hihn, Daniel A. Braun
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.
1 code implementation • 25 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.
Ranked #1 on Domain-IL Continual Learning on Cifar10 (5 tasks)
no code implementations • 30 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).
no code implementations • 3 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.
1 code implementation • 24 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.
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.
no code implementations • 26 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.
no code implementations • 8 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.
no code implementations • 16 Sep 2018 • Sebastian Gottwald, Daniel A. Braun
Specialization and hierarchical organization are important features of efficient collaboration in economical, artificial, and biological systems.
no code implementations • 4 Sep 2018 • Heinke Hihn, Sebastian Gottwald, Daniel A. Braun
Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power.
no code implementations • 16 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.
no code implementations • 7 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.
no code implementations • 21 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.
no code implementations • 5 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.
no code implementations • 24 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.
no code implementations • 16 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.