Yuan Wang

Metasurface-Enabled On-Chip Quantum Entanglement

2017 · Conference on Lasers and Electro-Optics

We report on on-chip quantum entanglement between two microscopically separated qubits by engineering their long-range interactions via a metasurface. The metasurface route to quantum state engineering opens a new paradigm for on-chip quantum technology.

Deep Semantic Network Representation

2020

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including …

Self-training and Label Propagation for Semi-supervised Classification

2023

Due to the high cost of manually labeling data and sometimes requiring domain expertise, semi-supervised methods have received a lot of attention. Self-training is a very effective semi-supervised method that greatly improves the problem of …

Hierarchical Episodic Control

2023 · Preprints.org

Deep reinforcement learning is one of the research hotspots in artificial intelligence and has been successfully applied in many research areas, however, the low training efficiency and high demand for samples are problems that limit …

Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression

2023 · arXiv (Cornell University)

Sparse linear regression methods for high-dimensional data commonly assume that residuals have constant variance, which can be violated in practice. For example, Aphasia Quotient (AQ) is a critical measure of language impairment and informs treatment …

An Aggregation-Free Federated Learning for Tackling Data Heterogeneity

2024 · arXiv (Cornell University)

The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by …

Enhancing Unsupervised Semantic Segmentation Through Context-Aware Clustering

2024 · IEEE Transactions on Multimedia

Despite the great progress of semantic segmentation with supervised learning, annotating large amounts of pixel-wise labels is, however, very expensive and time-consuming. To this end, Unsupervised Semantic Segmentation(USS) has been proposed to learn semantic segmentation, …