78 citations · 132 across the 10 of their papers we have counts for
11 papers
Convolutional dynamical sampling and some new results
Longxiu Huang, A. Martina Neuman, Sui Tang +1
In this work, we explore the dynamical sampling problem on driven by a convolution operator defined by a convolution kernel. This problem is inspired by the ne…
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
Wenzhuo Tang, Haitao Mao, Danial Dervovic +4
Models for natural language and images benefit from data scaling behavior: the more data fed into the model, the better they perform. This 'better with more' phenomenon enables the…
Single-Cell Multimodal Prediction via Transformers
Wenzhuo Tang, Hongzhi Wen, Renming Liu +5
The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding…
Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation
Hongzhi Wen, Wenzhuo Tang, Wei Jin +7
Spatially resolved transcriptomics brings exciting breakthroughs to single-cell analysis by providing physical locations along with gene expression. However, as a cost of the extre…
Deep Learning in Single-Cell Analysis
Dylan Molho, Jiayuan Ding, Zhaoheng Li +12
Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneou…
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Leveraging the fact that speaker identity and content vary on different time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables to symbolize these two attri…
Theoretical guarantees for the advantage of GNNs over NNs in generalizing bandlimited functions on Euclidean cubes
A. Martina Neuman, Rongrong Wang, Yuying Xie
Graph Neural Networks (GNNs) have emerged as formidable resources for processing graph-based information across diverse applications. While the expressive power of GNNs has traditi…
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) present…
Complex Recurrent Variational Autoencoder with Application to Speech Enhancement
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
As an extension of variational autoencoder (VAE), complex VAE uses complex Gaussian distributions to model latent variables and data. This work proposes a complex recurrent VAE fra…
Graph Neural Networks for Multimodal Single-Cell Data Integration
Hongzhi Wen, Jiayuan Ding, Wei Jin +3
Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular sta…
Themes Informed Audio-visual Correspondence Learning
Runze Su, Fei Tao, Xudong Liu +6
The applications of short-term user-generated video (UGV), such as Snapchat, and Youtube short-term videos, booms recently, raising lots of multimodal machine learning tasks. Among…