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Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning
Here we propose a new framework for predictive coding called active predictive coding which can learn hierarchical world models and solve two radically different open problems in AI: (1) how do we learn compositional representations, e. g., part-whole hierarchies, for equivariant vision?
Hyper-Universal Policy Approximation: Learning to Generate Actions from a Single Image using Hypernets
Inspired by Gibson's notion of object affordances in human vision, we ask the question: how can an agent learn to predict an entire action policy for a novel object or environment given only a single glimpse?
Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies
We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligence and brain modeling: how can neural networks learn intrinsic reference frames for objects and parse visual scenes into part-whole hierarchies by dynamically allocating nodes in a parse tree?
Learning a Convolutional Bilinear Sparse Code for Natural Videos
Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner.
Transformational Sparse Coding
Instead of discarding the rich and useful information about feature transformations to achieve invariance, we argue that models should learn object features conjointly with their transformations to achieve equivariance.
