Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

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 …

Generative models have been widely studied in computer vision. Recently, diffusion models
have drawn substantial attention due to the high quality of their generated images. A key …

In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically,
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …

The key idea behind the unsupervised learning of disentangled representations is that real-
world data is generated by a few explanatory factors of variation which can be recovered by …

Pyro is a probabilistic programming language built on Python as a platform for developing
advanced probabilistic models in AI research. To scale to large data sets and high …

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual
concepts, words, and semantic parsing of sentences without explicit supervision on any of …

We marry two powerful ideas: deep representation learning for visual recognition and
language understanding, and symbolic program execution for reasoning. Our neural …

We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …