The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of
software to support computational experiments 1 . To address this, we present Empirical …

The ability to combine known skills to create new ones may be crucial in the solution of
complex reinforcement learning problems that unfold over extended periods. We argue that a …

Emergent communication in artificial agents has been studied to understand language
evolution, as well as to develop artificial systems that learn to communicate with humans. We …

Traditional automated theorem proving systems for first-order logic depend on speed-optimized
search and many handcrafted heuristics designed to work over a wide range of domains…

A major challenge in applying machine learning to automated theorem proving is the scarcity
of training data, which is a key ingredient in training successful deep learning models. To …

A major challenge in applying machine learning to automated theorem proving is the scarcity
of training data, which is a key ingredient in training successful deep learning models. To …

Discrete amorphous materials are best described in terms of arbitrary networks which can
be embedded in three dimensional space. Investigating the thermodynamic equilibrium as …

We explore the use of Graph Neural Networks (GNNs) for learning representations of
propositional and first-order logical formulae. Traditional non-graphical based approaches like …

k-nearest neighbor and centroid-based classification algorithms are frequently used in text
classification due to their simplicity and performance. While k-nearest neighbor algorithm …

Generalized preferential attachment is defined as the tendency of a vertex to acquire new
links in the future with respect to a particular vertex property. Understanding which properties …