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Found 12 papers, 1 papers with code
Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Pictures
We present an exploration of machine learning architectures for predicting brain responses to realistic images on occasion of the Algonauts Challenge 2023.
Multimodal Integration of Olfactory and Visual Processing through DCM analysis: Contextual Modulation of Facial Perception
The proposed methodology provides insights into the neural mechanisms underlying the integration of visual and olfactory stimuli in face processing.
Learning to Prompt in the Classroom to Understand AI Limits: A pilot study
Encouraging preliminary results emerged, including high appreciation of the activity, improved interaction quality with the LLM, reduced negative AI sentiments, and a better grasp of limitations, specifically unreliability, limited understanding of commands leading to unsatisfactory responses, and limited presentation flexibility.
Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix)
Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format.
Influence of the Geometry of the world model on Curiosity Based Exploration
In human spatial awareness, 3-D projective geometry structures information integration and action planning through perspective taking within an internal representation space.
World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges
Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics.
Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy
Assuming that learning of beliefs can take place by observing own decision and beliefs estimation processes in partially observable environments and using a simple feed-forward deep learning model, we show that when learning to predict others' intentions and actions, faster and more accurate predictions can be acquired if beliefs attribution is learnt simultaneously with action and intentions prediction.
A Classification Methodology based on Subspace Graphs Learning
The objective is to select the best structures created during the training phase using an ensemble of spanning trees.
Transferring Adaptive Theory of Mind to social robots: insights from developmental psychology to robotics
Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios.
Functional advantages of an adaptive Theory of Mind for robotics: a review of current architectures
The aim of the present paper was to determine in which way and how often ToM features are integrated in the architectures analyzed, and if they provide robots with the associated functional advantages.
Proactive Intention Recognition for Joint Human-Robot Search and Rescue Missions through Monte-Carlo Planning in POMDP Environments
Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments.
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
One-class classifiers are trained with target class only samples.
