Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

Floods result in substantial damage throughout the world every year. Accurate predictions of
floods can significantly alleviate casualties and property losses. However, due to the …

Current advances in Artificial Intelligence and machine learning in general, and deep
learning in particular have reached unprecedented impact not only across research …

Recent research in mechanistic interpretability has attempted to reverse-engineer
Transformer models by carefully inspecting network weights and activations. However, these …

Deep Learning (DL) has achieved tremendous success in many cutting-edge applications.
However, the state-of-the-art DL systems still suffer from quality issues. While some recent …

We propose a novel method for explaining the predictions of any classifier. In our approach,
local explanations are expected to explain both the outcome of a prediction and how that …

Deep neural-network-based language models (LMs) are increasingly applied to large-scale
protein sequence data to predict protein function. However, being largely black-box models …

The interpretability of deep learning models has raised extended attention these years. It will
be beneficial if we can learn an interpretable structure from deep learning models. In this …

Abstract Recurrent Neural Networks (RNNs) have achieved tremendous success in
processing sequential data, yet understanding and analyzing their behaviours remains a …