Search Results for author:
Found 9 papers, 3 papers with code
Prerequisite Structure Discovery in Intelligent Tutoring Systems
This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems.
Unsupervised Motion Retargeting for Human-Robot Imitation
This early-stage research work aims to improve online human-robot imitation by translating sequences of joint positions from the domain of human motions to a domain of motions achievable by a given robot, thus constrained by its embodiment.
On the Relationship Between Variational Inference and Auto-Associative Memory
In this article, we propose a variational inference formulation of auto-associative memories, allowing us to combine perceptual inference and memory retrieval into the same mathematical framework.
Intrinsically Motivated Learning of Causal World Models
Despite the recent progress in deep learning and reinforcement learning, transfer and generalization of skills learned on specific tasks is very limited compared to human (or animal) intelligence.
A Predictive Coding Account for Chaotic Itinerancy
As a phenomenon in dynamical systems allowing autonomous switching between stable behaviors, chaotic itinerancy has gained interest in neurorobotics research.
Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference
In this work, we build upon the Active Inference (AIF) and Predictive Coding (PC) frameworks to propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories.
Autonomous learning and chaining of motor primitives using the Free Energy Principle
In this article, we apply the Free-Energy Principle to the question of motor primitives learning.
State representation learning with recurrent capsule networks
Unsupervised learning of compact and relevant state representations has been proved very useful at solving complex reinforcement learning tasks.
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents.
