Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

Neural networks for NLP are becoming increasingly complex and widespread, and there is a
growing concern if these models are responsible to use. Explaining models helps to address …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …

In the Transformer model," self-attention" combines information from attended embeddings
into the representation of the focal embedding in the next layer. Thus, across layers of the …

AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

With the growing popularity of deep-learning based NLP models, comes a need for
interpretable systems. But what is interpretability, and what constitutes a high-quality …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

State-of-the-art models in NLP are now predominantly based on deep neural networks that
are opaque in terms of how they come to make predictions. This limitation has increased …

Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …