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On the Binding Problem in Artificial Neural Networks
Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber
Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for…
Unsupervised Object Keypoint Learning using Local Spatial Predictability
Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber
We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to ide…
Learning Associative Inference Using Fast Weight Memory
Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber
Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such…
Hierarchical Relational Inference
Aleksandar Stanić, Sjoerd van Steenkiste, Jürgen Schmidhuber
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics. The notion of an abstract object, however, encompasses a w…
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber
Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functi…
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits
Aditya Ramesh, Paulo Rauber, Michelangelo Conserva +1
An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experienc…