31 citations · 64 across the 14 of their papers we have counts for
14 papers
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
Shahana Ibrahim, Tri Nguyen, Xiao Fu
Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simul…
Under-Counted Tensor Completion with Neural Incorporation of Attributes
Shahana Ibrahim, Xiao Fu, Rebecca Hutchinson +1
Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated…
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
Tri Nguyen, Shahana Ibrahim, Xiao Fu
The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., (DCC) -- has proven effectiv…
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
Subash Timilsina, Sagar Shrestha, Xiao Fu
Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e.g., frequency and space) radio power propagation maps from limited sensor meas…
Provable Subspace Identification Under Post-Nonlinear Mixtures
Qi Lyu, Xiao Fu
Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixt…
On Local Linear Convergence of Projected Gradient Descent for Unit-Modulus Least Squares
Trung Vu, Raviv Raich, Xiao Fu
The unit-modulus least squares (UMLS) problem has a wide spectrum of applications in signal processing, e.g., phase-only beamforming, phase retrieval, radar code design, and sensor…
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis
Qi Lyu, Xiao Fu
Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the…
On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR
Zhao Yang, Dianwen Ng, Xiao Fu +5
End-to-end automatic speech recognition (ASR) has achieved promising results. However, most existing end-to-end ASR methods neglect the use of specific language characteristics. Fo…
Uncovering migration systems through spatio-temporal tensor co-clustering
Zack W. Almquist, Tri Duc Nguyen, Mikael Sorensen +2
A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between tw…
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types
Shentong Mo, Xi Fu, Chenyang Hong +6
In the genome biology research, regulatory genome modeling is an important topic for many regulatory downstream tasks, such as promoter classification, transaction factor binding s…
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis
Sagar Shrestha, Xiao Fu
Classic and deep generalized canonical correlation analysis (GCCA) algorithms seek low-dimensional common representations of data entities from multiple ``views'' (e.g., audio and…
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Qi Lyu, Xiao Fu, Weiran Wang +1
Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancin…
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems
Cheng Qian, Xiao Fu, Nicholas D. Sidiropoulos
We consider downlink (DL) channel estimation for frequency division duplex based massive MIMO systems under the multipath model. Our goal is to provide fast and accurate channel es…
Data-Driven Learning-Based Optimization for Distribution System State Estimation
Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos
Distribution system state estimation (DSSE) is a core task for monitoring and control of distribution networks. Widely used algorithms such as Gauss-Netwon perform poorly with the…