Yu Shi

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

2018 · arXiv (Cornell University)

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the …

Method for Predicting Compound Properties in Drug Development Based on Machine Learning

2021

Data on drugs, targets and indications were collected from the database, and gene expression profile data processed by 1309 small drug molecules were collected from the connectivity map. The biclustering algorithm was used to cluster …

Rethinking Dense Retrieval's Few-Shot Ability

2023 · arXiv (Cornell University)

Few-shot dense retrieval (DR) aims to effectively generalize to novel search scenarios by learning a few samples. Despite its importance, there is little study on specialized datasets and standardized evaluation protocols. As a result, current …

Entanglement-Assisted Coding for Arbitrary Linear Computations Over a Quantum MAC

2025 · arXiv (Cornell University)

We study a linear computation problem over a quantum multiple access channel (LC-QMAC), where $S$ servers share an entangled state and separately store classical data streams $W_1,\cdots, W_S$ over a finite field $\mathbb{F}_d$. A user …