We propose a causal interpretation of self-attention in the Transformer neural network
architecture. We interpret self-attention as a mechanism that estimates a structural equation …

With the advent of universal function approximators in the domain of reinforcement learning,
the number of practical applications leveraging deep reinforcement learning (DRL) has …

Large language models (LLMs) have excelled in various natural language processing tasks,
but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes …

Abstract Dynamic Causal Models (DCMs) in functional Magnetic Resonance Imaging (fMRI)
decipher causal interactions, known as Effective Connectivity, among neuronal populations …

Scientific research often seeks to understand the causal structure underlying high-level
variables in a system. For example, climate scientists study how phenomena, such as El Ni …

This paper discusses the environment anomalies that the CARL/COLDArm system was
subjected to utilizing the OceanWATERS simulator to demonstrate" fail-active" autonomy …

Climate models, such as Earth system models (ESMs), are crucial for simulating future
climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse …

While typical validation and verification approaches focus on identifying the associations
between data elements using statistical and machine learning methods, the novel methods …