Image-based cell phenoty** relies on quantitative measurements as encoded
representations of cells; however, defining suitable representations that capture complex …

Equivariant neural networks, whose hidden features transform according to representations
of a group $ G $ acting on the data, exhibit training efficiency and an improved …

Deep auto-encoders (DAEs) have achieved great success in learning data representations
via the powerful representability of neural networks. But most DAEs only focus on the most …

We introduce a general method for learning representations that are equivariant to
symmetries of data. Our central idea is to decompose the latent space into an invariant factor …

Incorporating geometric inductive biases into models can aid interpretability and
generalization, but encoding to a specific geometric structure can be challenging due to the …

Recent research has demonstrated the significance of incorporating invariance into neural
networks. However, existing methods require direct sampling over the entire transformation …

Image-based cell phenoty** relies on quantitative measurements as encoded
representations of cells; however, defining suitable representations that capture complex …

Unsupervised representation learning on multi-view data (multiple types of features or
modalities) becomes a compelling topic in machine learning. Most existing methods focus …

Abstract We introduce Equivariant Isomorphic Networks (EquIN)–a method for learning
representations that are equivariant with respect to general group actions over data …

We address the problem of learning representations from observations of a scene involving
an agent and an external object the agent interacts with. To this end, we propose a …