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Found 8 papers, 6 papers with code
Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task
This not only provides a generative model of eye movements in this task but also suggests a computational theory for how humans solve the task, namely that humans use mental simulation.
Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience
In recent years, trends towards studying simulated games have gained momentum in the fields of artificial intelligence, cognitive science, psychology, and neuroscience.
Unsupervised Model Selection for Variational Disentangled Representation Learning
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks.
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms.
MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations.
Visual Interaction Networks: Learning a Physics Simulator from Video
We introduce the Visual Interaction Network, a general-purpose model for learning the dynamics of a physical system from raw visual observations.
Visual Interaction Networks
We found that from just six input video frames the Visual Interaction Network can generate accurate future trajectories of hundreds of time steps on a wide range of physical systems.
