Related papers: Interpretable machine learning for finding interme…
Interpretability in machine learning models is important in high-stakes decisions, such as whether to order a biopsy based on a mammographic exam. Mammography poses important challenges that are not present in other computer vision tasks:…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for…
The rapid adoption of complex Artificial Intelligence (AI) and Machine Learning (ML) models has led to their characterization as black boxes due to the difficulty of explaining their internal decision-making processes. This lack of…
Here we investigate the possibility that some globular clusters (GCs) harbor intermediate mass black holes (BH) in their centers and are also embedded in a low-mass dark matter (DM) halo. Up to date, there is no evidence on whether or not…
During the last decades, much effort has been devoted to explain the discrepancy between the amount of intracluster medium (ICM) estimated from stellar evolution theories and that emerging from observations in globular clusters (GCs). One…
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve…
Many observed globular clusters (GCs) seem to show a central overabundance of mass whose nature has not yet fully understood. Indeed, it is not clear whether it is due to a central intermediate mass black hole (IMBH) or to a massive stellar…
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy.…
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their…
Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in…
Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict concepts and then use them to perform a downstream task.…
Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we…
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of…
With the advancement of technology for artificial intelligence (AI) based solutions and analytics compute engines, machine learning (ML) models are getting more complex day by day. Most of these models are generally used as a black box…
The dynamical evolution of globular clusters (GCs) is tied to their binary population, as binaries segregate to the cluster centre, leading to an increased binary fraction in the core. This central overabundance of mainly hard binaries can…
Intermediate mass black holes (IMBHs) are an (as yet) elusive class of black holes that are expected to lie in the $10^2-10^5\,M_{\odot}$ range, between the firmly established stellar-mass black holes and ${\gtrsim}10^6\,M_{\odot}$…
Intermediate mass black holes (IMBHs) have masses between the $10^2\!-\!10^6$ M$_\odot$ and are key to our understanding of the formation of massive black holes. The known population of IMBH remains small, with a few hundred candidates and…
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions…
We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching, a longstanding tool in observational causal inference. Interpretability is paramount in…