The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 this …

Both Minsky's" society of mind" and Schmidhuber's" learning to think" inspire diverse
societies of large multimodal neural networks (NNs) that solve problems by interviewing …

Human visual reasoning is characterized by an ability to identify abstract patterns from only
a small number of examples, and to systematically generalize those patterns to novel inputs …

We present VAEL, a neuro-symbolic generative model integrating variational autoencoders
(VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides …

In this work, we present a neural approach to reconstructing rooted tree graphs describing
hierarchical interactions, using a novel representation we term the lowest common ancestor …

We propose PermaKey, a novel approach to representation learning based on object
keypoints. It leverages the predictability of local image regions from spatial neighborhoods …

Reinforcement learning agents must generalize beyond their training experience. Prior work
has focused mostly on identical training and evaluation environments. Starting from the …

The locations of objects and their associated landmark keypoints can serve as versatile and
semantically meaningful image representations. In natural scenes, these keypoints are often …

Current state-of-the-art synchrony-based models encode object bindings with complex-
valued activations and compute with real-valued weights in feedforward architectures. We …