Erik Derner
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View article: Can ChatGPT read who you are?
Can ChatGPT read who you are? Open
The interplay between artificial intelligence (AI) and psychology, particularly in personality assessment, represents an important emerging area of research. Accurate personality trait estimation is crucial not only for enhancing personali…
View article: SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture
SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture Open
Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression. Evolutionary methods such as genetic programming have been com…
View article: A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models Open
As large language models (LLMs) permeate more and more applications, an assessment of their associated security risks becomes increasingly necessary. The potential for exploitation by malicious actors, ranging from disinformation to data b…
View article: Can ChatGPT Read Who You Are?
Can ChatGPT Read Who You Are? Open
The interplay between artificial intelligence (AI) and psychology, particularly in personality assessment, represents an important emerging area of research. Accurate personality trait estimation is crucial not only for enhancing personali…
View article: A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models Open
As large language models (LLMs) permeate more and more applications, an assessment of their associated security risks becomes increasingly necessary. The potential for exploitation by malicious actors, ranging from disinformation to data b…
View article: Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression Open
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. H…
View article: Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression Open
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. H…
View article: SymFormer: End-to-end symbolic regression using transformer-based architecture
SymFormer: End-to-end symbolic regression using transformer-based architecture Open
Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied to symbolic reg…
View article: Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning
Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning Open
Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, locali…
View article: Symbolic Regression Methods for Reinforcement Learning
Symbolic Regression Methods for Reinforcement Learning Open
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the valu…
View article: Selecting Informative Data Samples for Model Learning Through Symbolic Regression
Selecting Informative Data Samples for Model Learning Through Symbolic Regression Open
Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the…
View article: Visual Navigation in Real-World Indoor Environments Using End-to-End\n Deep Reinforcement Learning
Visual Navigation in Real-World Indoor Environments Using End-to-End\n Deep Reinforcement Learning Open
Visual navigation is essential for many applications in robotics, from\nmanipulation, through mobile robotics to automated driving. Deep reinforcement\nlearning (DRL) provides an elegant map-free approach integrating image\nprocessing, loc…
View article: Symbolic Regression for Constructing Analytic Models in Reinforcement Learning
Symbolic Regression for Constructing Analytic Models in Reinforcement Learning Open
Reinforcement learning (RL) is a widely used approach for controlling systems with unknown or time-varying dynamics. Even though RL does not require a model of the system, it is known to be faster and safer when using models learned online…