The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

This paper studies the curious phenomenon for machine learning models with Transformer
architectures that their activation maps are sparse. By activation map we refer to the …

Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …

Classical simulation of quantum dynamics plays an important role in our understanding of
quantum complexity, and in the development of quantum technologies. Compared to other …

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising
incidence rate in the developed world. Molecular profiling techniques have been used to …

Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language
model to a downstream application. However, it uses the same dataset-level tuned prompt …

Overparametrization is one of the most surprising and notorious phenomena in machine
learning. Recently, there have been several efforts to study if, and how, quantum neural …

Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown
to be effective in unsupervised phrase-structure grammar induction. However, due to the …

K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets
stuck easily in spurious local minima, and 2) the number of clusters has to be given a priori …