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 …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

State-of-the-art neural language models can now be used to solve ad-hoc language tasks
through zero-shot prompting without the need for supervised training. This approach has …

Deep learning (DL) models have achieved impressive performance in various domains such
as medicine, finance, and autonomous vehicle systems with advances in computing power …

Long short‐term memory (LSTM) networks represent one of the most prevalent deep
learning (DL) architectures in current hydrological modeling, but they remain black boxes …

With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …

The lack of transparency of powerful Machine Learning systems paired with their growth in
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …

Breakthroughs in transformer-based models have revolutionized not only the NLP field, but
also vision and multimodal systems. However, although visualization and interpretability …

The rapid development of deep natural language processing (NLP) models for text
classification has led to an urgent need for a unified understanding of these models …

We propose CX-ToM, short for counterfactual explanations with theory-of-mind, a new
explainable AI (XAI) framework for explaining decisions made by a deep convolutional …