The abstract basis of modern computation is the formal description of a finite state machine,
the Universal Turing Machine, based on manipulation of integers and logic symbols. In this …

Symbolic regression is a machine learning technique that can learn the equations governing
data and thus has the potential to transform scientific discovery. However, symbolic …

Occupational fraud within companies currently causes losses of around 5% of company
revenue each year. While enterprise resource planning systems can enable automated …

Neural Networks have traditionally been used to handle numerical information based on
their training. However, they often struggle with systematic generalization, particularly when …

The human ability to learn and reuse skills in a systematic manner is critical to our daily
routines. For example, having the skills for executing the basic arithmetic operations …

Neural Arithmetic Logic Modules have become a growing area of interest, though remain a
niche field. These modules are neural networks which aim to achieve systematic …

Abstract Conventional Neural Networks can approximate simple arithmetic operations, but
fail to generalize beyond the range of numbers that were seen during training. Neural …

In this paper, we investigate the extrapolation capabilities of implicit deep learning models in
handling unobserved data, where traditional deep neural networks may falter. Implicit …

The immense real-time applicability of Python coding makes the task of evaluating the code
highly intriguing, in the Natural Language Processing (NLP) domain. Evaluation of computer …

Deep neural networks excel on a variety of different tasks, often surpassing human abilities.
However, when presented with out-of-distribution data, these models tend to break down …