A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general
understanding of the world. Such an agent would require the ability to continually …

Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …

Despite achieving tremendous success, existing deep learning models have exposed
limitations in compositional generalization, the capability to learn compositional rules and …

We propose a method for meta-learning reinforcement learning algorithms by searching
over the space of computational graphs which compute the loss function for a value-based …

Visual reasoning tasks such as visual question answering (VQA) require an interplay of
visual perception with reasoning about the question semantics grounded in perception …

Combinatorial optimization algorithms for graph problems are usually designed afresh for
each new problem with careful attention by an expert to the problem structure. In this work …

Abstract The field of Neural Combinatorial Optimization (NCO) offers multiple learning-
based approaches to solve well-known combinatorial optimization tasks such as Traveling …

Graph neural networks (GNNs) are effective models for many dynamical systems consisting
of entities and relations. Although most GNN applications assume a single type of entity and …