In this paper we show how tensor networks help in developing explainability
of machine learning algorithms. Specifically, we develop an unsupervised
clustering algorithm based on Matrix Product States (MPS) and apply it in the
context of a real use-case of adversary-generated threat intelligence. Our
investigation proves that MPS rival traditional deep learning models such as
autoencoders and GANs in terms of performance, while providing much richer
model interpretability. Our approach naturally facilitates the extraction of
feature-wise probabilities, Von Neumann Entropy, and mutual information,
offering a compelling narrative for classification of anomalies and fostering
an unprecedented level of transparency and interpretability, something
fundamental to understand the rationale behind artificial intelligence
decisions.
Tensor Networks and Explainability in Machine Learning
In recent years, the field of machine learning has witnessed tremendous advancements, with complex models such as deep neural networks achieving state-of-the-art performance in various tasks. However, one significant challenge that arises with these powerful models is their lack of interpretability. As artificial intelligence continues to be integrated into critical domains such as finance, healthcare, and security, the need for explainable AI becomes increasingly important.
In this groundbreaking paper, the authors demonstrate how tensor networks can play a vital role in developing explainability for machine learning algorithms. Tensor networks, a multi-disciplinary concept at the intersection of physics and computer science, provide a framework for representing and analyzing high-dimensional data structures.
The authors utilize Matrix Product States (MPS) – a specific form of tensor network – to develop an unsupervised clustering algorithm. They apply this algorithm to a real-world use-case involving adversary-generated threat intelligence. By leveraging MPS, the proposed approach outperforms traditional deep learning models like autoencoders and GANs in terms of performance.
However, the real value of this approach lies in its ability to provide richer model interpretability. The authors highlight how tensor networks facilitate the extraction of feature-wise probabilities, Von Neumann Entropy, and mutual information. These metrics enable a compelling narrative for anomaly classification and offer a previously unseen level of transparency and interpretability.
Transparency and interpretability are crucial aspects for trusting AI systems, particularly when it comes to critical decision-making. The ability to understand and explain the rationale behind artificial intelligence decisions can significantly impact user acceptance and regulatory compliance.
Furthermore, the multi-disciplinary nature of tensor networks highlights the importance of integrating knowledge from various domains. The combination of concepts from physics, mathematics, and computer science allows researchers to tackle complex problems in a holistic manner.
Looking ahead, this research paves the way for further advancements in explainable AI. By leveraging tensor networks and their inherent interpretability, researchers can develop models that not only achieve high performance but also provide actionable insights and explanations for their decisions.
The implications of this work extend beyond the field of machine learning. The interdisciplinary nature of tensor networks opens up exciting possibilities for applications in physics, chemistry, and even social sciences. This convergence of disciplines has the potential to drive innovation and shed new light on fundamental questions.
“Our investigation proves that MPS rival traditional deep learning models such as autoencoders and GANs in terms of performance, while providing much richer model interpretability.”
In conclusion, this research highlights the importance of explainability in machine learning and showcases how the multi-disciplinary nature of tensor networks can address this challenge. By incorporating concepts from diverse fields, researchers can create models with both high performance and interpretability, paving the way for ethical and transparent artificial intelligence systems.