Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Explaining machine learning models with interactive natural language conversations using TalkToModel
Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …
more complex and harder to understand. To understand complex models, researchers have …
Ai alignment: A comprehensive survey
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 …
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
Applying interpretable machine learning in computational biology—pitfalls, recommendations and opportunities for new developments
Recent advances in machine learning have enabled the development of next-generation
predictive models for complex computational biology problems, thereby spurring the use of …
predictive models for complex computational biology problems, thereby spurring the use of …
Post hoc explanations of language models can improve language models
Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities in
performing complex tasks. Moreover, recent research has shown that incorporating human …
performing complex tasks. Moreover, recent research has shown that incorporating human …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations
A critical problem in the field of post hoc explainability is the lack of a common foundational
goal among methods. For example, some methods are motivated by function approximation …
goal among methods. For example, some methods are motivated by function approximation …
Post-hoc explanations fail to achieve their purpose in adversarial contexts
Existing and planned legislation stipulates various obligations to provide information about
machine learning algorithms and their functioning, often interpreted as obligations to …
machine learning algorithms and their functioning, often interpreted as obligations to …
Logic-based explainability in machine learning
J Marques-Silva - … Knowledge: 18th International Summer School 2022 …, 2023 - Springer
The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …