Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Recent strategies achieved ensembling"" for free"" by fitting concurrently diverse
subnetworks inside a single base network. The main idea during training is that each …

With growing deployment of machine learning (ML) models, ML developers are training or re-
training increasingly more deep neural networks (DNNs). They do so to find the most …

We present a self-designing key-value storage engine, Cosine, which can always take the
shape of the close to" perfect" engine architecture given an input workload, a cloud budget …

Tissue microarray (TMA) core images are a treasure trove for artificial intelligence
applications. However, a common problem of TMAs is multiple sectioning, which can …

With the advancement of satellite technology, a considerable amount of very high-resolution
imagery has become available to be used for the Land Cover and Land Use (LCLU) …

Convolutional neural networks are utilized to solve increasingly more complex problems
and with more data. As a result, researchers and practitioners seek to scale the …

Key-value data structures constitute the core of any datadriven system. They provide the
means to store, search, and modify data residing at various levels of the storage and …

Machine learning is becoming more prevalent in safety-critical systems like autonomous
vehicles and medical imaging. Faulty training data, where data is either misla-belled …

We introduce Symbiosis, a framework for key-value storage systems that dynamically
configures application and kernel cache sizes to improve performance. We integrate …