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Found 12 papers, 6 papers with code
Single-Cell Multimodal Prediction via Transformers
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 of cellular states and dynamics.
Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation
In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder
For the prior of speaker identity variable, \acrshort{fhvae} assumes it is a Gaussian distribution with an utterance-scale varying mean and a fixed variance.
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
Superiority of GNN over NN in generalizing bandlimited functions
Graph Neural Networks (GNNs) have emerged as formidable resources for processing graph-based information across diverse applications.
Complex Recurrent Variational Autoencoder with Application to Speech Enhancement
This work proposes a complex recurrent VAE framework, specifically in which complex-valued recurrent neural network and L1 reconstruction loss are used.
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective
As a self-supervised objective, autoregressive predictive coding (APC), on the other hand, has been used in extracting meaningful and transferable speech features for multiple downstream tasks.
Graph Neural Networks for Multimodal Single-Cell Data Integration
Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.
Themes Informed Audio-visual Correspondence Learning
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.
Manifold Denoising by Nonlinear Robust Principal Component Analysis
This paper extends robust principal component analysis (RPCA) to nonlinear manifolds.
Exact Cluster Recovery via Classical Multidimensional Scaling
Classical multidimensional scaling is an important dimension reduction technique.
Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics
In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent.
