Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture,
healthcare, entertainment, and other industries. Most of these systems are developed with …

In many contexts, simpler models are preferable to more complex models and the control of
this model complexity is the goal for many methods in machine learning such as …

In statistical learning theory, a generalization bound usually involves a complexity measure
imposed by the considered theoretical framework. This limits the scope of such bounds, as …

This work targets designing a principled and unified training-free framework for Neural
Architecture Search (NAS), with high performance, low cost, and in-depth interpretation …

Explaining the generalization characteristics of deep learning is an emerging topic in
advanced machine learning. There are several unanswered questions about how learning …

Neural networks are known to exploit spurious artifacts (or shortcuts) that co-occur with a
target label, exhibiting heuristic memorization. On the other hand, networks have been …

Hybrid Quantum Neural Networks (HQNNs) have gained attention for their potential to
enhance computational performance by incorporating quantum layers into classical neural …

Abstract Machine learning and deep learning classification models are data-driven, and the
model and the data jointly determine their classification performance. It is biased to evaluate …

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence
(AI) and machine learning (ML), particularly as these models are increasingly deployed in …

Single cell RNA-seq data is an important source for profiling cellular heterogeneity.
Clustering is an important step in any single cell pipeline because it allows us to discover …