Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Interpreting and explaining the behavior of deep neural networks is critical for many tasks.
Explainable AI provides a way to address this challenge, mostly by providing per-pixel …

There is growing interest in concept-based models (CBMs) that combine high-performance
and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A …

The key to high-level cognition is believed to be the ability to systematically manipulate and
compose knowledge pieces. While token-like structured knowledge representations are …

Abstract Neuro-Symbolic (NeSy) predictive models hold the promise of improved
compliance with given constraints, systematic generalization, and interpretability, as they …

Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many
issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal …

While traditional deep learning models often lack interpretability, concept bottleneck models
(CBMs) provide inherent explanations via their concept representations. Specifically, they …

Research on Explainable Artificial Intelligence has recently started exploring the idea of
producing explanations that, rather than being expressed in terms of low-level features, are …

Existing fine-grained hashing methods typically lack code interpretability as they compute
hash code bits holistically using both global and local features. To address this limitation we …

Visual planning simulates how humans make decisions to achieve desired goals in the form
of searching for visual causal transitions between an initial visual state and a final visual …