Abstract Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with
the aim of creating machines capable of performing any intellectual task that humans can …

Despite enormous progress in machine learning, artificial neural networks still lag behind
brains in their ability to generalize to new situations. Given identical training data …

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 …

Convolution has been the core ingredient of modern neural networks, triggering the surge of
deep learning in vision. In this work, we rethink the inherent principles of standard …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

A possible explanation for the impressive performance of masked language model (MLM)
pre-training is that such models have learned to represent the syntactic structures prevalent …

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …

Training a neural network is synonymous with learning the values of the weights. By
contrast, we demonstrate that randomly weighted neural networks contain subnetworks …

We propose a new family of efficient and expressive deep generative models of graphs,
called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one …

Recently, there has been a growing interest in automating the process of neural architecture
design, and the Differentiable Architecture Search (DARTS) method makes the process …