Search Results for author: Xiao Fu

Found 53 papers, 14 papers with code

Under-Counted Tensor Completion with Neural Incorporation of Attributes

1 code implementation5 Jun 2023 Shahana Ibrahim, Xiao Fu, Rebecca Hutchinson, Eugene Seo

Systematic under-counting effects are observed in data collected across many disciplines, e. g., epidemiology and ecology.


Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization

1 code implementation5 Jun 2023 Shahana Ibrahim, Tri Nguyen, Xiao Fu

The contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented.

Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach

1 code implementation30 May 2023 Tri Nguyen, Shahana Ibrahim, Xiao Fu

The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i. e., $\textit{deep constrained clustering}$ (DCC) -- has proven effective for incorporating weak supervision into massive data clustering: Less than 1% of pair similarity annotations can often substantially enhance the clustering accuracy.

Clustering Deep Clustering

Quantized Radio Map Estimation Using Tensor and Deep Generative Models

1 code implementation3 Mar 2023 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 measurements.

Spectrum Cartography Tensor Decomposition

Provable Subspace Identification Under Post-Nonlinear Mixtures

no code implementations14 Oct 2022 Qi Lyu, Xiao Fu

In this work, the post-nonlinear (PNL) mixture model -- where unknown element-wise nonlinear functions are imposed onto a linear mixture -- is revisited.

Causal Discovery Speech Separation

On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

no code implementations14 Jun 2022 Qi Lyu, Xiao Fu

Our framework also takes the learning function's approximation error into consideration, and reveals an intuitive trade-off between the complexity and expressiveness of the employed function learner.

Contrastive Learning Disentanglement +1

Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning

no code implementations11 Jun 2022 Sagar Shrestha, Xiao Fu, Mingyi Hong

This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI).

Imitation Learning

Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing

no code implementations8 May 2022 Meng Ding, Xiao Fu, Xi-Le Zhao

However, existing LL1-based HU algorithms use a three-factor parameterization of the tensor (i. e., the hyperspectral image cube), which leads to a number of challenges including high per-iteration complexity, slow convergence, and difficulties in incorporating structural prior information.

Hyperspectral Unmixing Tensor Decomposition

Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

1 code implementation29 Mar 2022 Xiao Fu, Shangzhan Zhang, Tianrun Chen, Yichong Lu, Lanyun Zhu, Xiaowei Zhou, Andreas Geiger, Yiyi Liao

In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives.

Instance Segmentation Scene Segmentation

On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR

no code implementations26 Jan 2022 Zhao Yang, Dianwen Ng, Xiao Fu, Liping Han, Wei Xi, Rui Wang, Rui Jiang, Jizhong Zhao

Based on the above intuition, we first investigate types of end-to-end encoder-decoder based models in the single-input dual-output (SIDO) multi-task framework, after which a novel asynchronous decoding with fuzzy Pinyin sampling method is proposed according to the one-to-one correspondence characteristics between Pinyin and Character.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis

1 code implementation25 Sep 2021 Sagar Shrestha, Xiao Fu

Compared to the unquantized version, our empirical study shows that the proposed algorithm enjoys a substantial reduction of communication overheads with virtually no loss in accuracy and convergence speed.

Distributed Computing Distributed Optimization +2

Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach

no code implementations23 Sep 2021 Tri Nguyen, Xiao Fu, Ruiyuan Wu

Our algorithm capitalizes on the special update rules of a classic algorithm from the 1950s, namely, the Frank-Wolfe (FW) algorithm.

Community Detection

Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization

no code implementations14 Jun 2021 Shahana Ibrahim, Xiao Fu

Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably labeling massive data.


Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective

1 code implementation ICLR 2022 Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu

Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities).

Clustering Disentanglement +2

Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models

1 code implementation1 May 2021 Sagar Shrestha, Xiao Fu, Mingyi Hong

However, such deep learning (DL)-based SC approaches encounter serious challenges in both off-line model learning (training) and completion (generalization), possibly because the latent state space for generating the radio maps is prohibitively large.

Spectrum Cartography

Stochastic Block-ADMM for Training Deep Networks

no code implementations1 May 2021 Saeed Khorram, Xiao Fu, Mohamad H. Danesh, Zhongang Qi, Li Fuxin

We prove the convergence of our proposed method and justify its capabilities through experiments in supervised and weakly-supervised settings.

Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses

no code implementations29 Apr 2021 Wenqiang Pu, Shahana Ibrahim, Xiao Fu, Mingyi Hong

This work offers a unified stochastic algorithmic framework for large-scale CPD decomposition under a variety of non-Euclidean loss functions.

Tensor Decomposition

Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors

no code implementations24 Feb 2021 Yu-Chun Miao, Xi-Le Zhao, Xiao Fu, Jian-Li Wang, Yu-Bang Zheng

Under the unsupervised DIP framework, it is hypothesized and empirically demonstrated that proper neural network structures are reasonable priors of certain types of images, and the network weights can be learned without training data.

Image Denoising

A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis

no code implementations11 Jan 2021 Xiao Fu, Guijun Zhang

We propose a new Named entity recognition (NER) method to effectively make use of the results of Part-of-speech (POS) tagging, Chinese word segmentation (CWS) and parsing while avoiding NER error caused by POS tagging error.

Chinese Named Entity Recognition Chinese Word Segmentation +5

Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment

4 code implementations16 Nov 2020 Haoran Sun, Wenqiang Pu, Minghe Zhu, Xiao Fu, Tsung-Hui Chang, Mingyi Hong

We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes.

Continual Learning Fairness

Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals

no code implementations30 Jun 2020 Shahana Ibrahim, Xiao Fu

Recent work has proposed to recover the joint probability mass function (PMF) of an arbitrary number of RVs from three-dimensional marginals, leveraging the algebraic properties of low-rank tensor decomposition and the (unknown) dependence among the RVs.

Tensor Decomposition

Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling

no code implementations18 Jun 2020 Meng Ding, Xiao Fu, Ting-Zhu Huang, Jun Wang, Xi-Le Zhao

This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.

Super-Resolution Tensor Decomposition

On Recoverability of Randomly Compressed Tensors with Low CP Rank

no code implementations8 Jan 2020 Shahana Ibrahim, Xiao Fu, Xingguo Li

Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model.

Compressive Sensing Video Compression

Spectrum Cartography via Coupled Block-Term Tensor Decomposition

no code implementations28 Nov 2019 Guoyong Zhang, Xiao Fu, Jun Wang, Xi-Le Zhao, Mingyi Hong

Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i. e., a radio map)---from limited samples taken sparsely over the region.

Spectrum Cartography Tensor Decomposition

Nonlinear Multiview Analysis: Identifiability and Neural Network-assisted Implementation

no code implementations19 Sep 2019 Qi Lyu, Xiao Fu

In this work, we revisit nonlinear multiview analysis and address both the theoretical and computational aspects.

Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation

1 code implementation2 Jul 2019 Ruiyuan Wu, Wing-Kin Ma, Xiao Fu, Qiang Li

Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of co-registered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively.


Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization

no code implementations16 Jan 2019 Xiao Fu, Shahana Ibrahim, Hoi-To Wai, Cheng Gao, Kejun Huang

In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations.

Stochastic Optimization

Learning Nonlinear Mixtures: Identifiability and Algorithm

no code implementations6 Jan 2019 Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Kejun Huang

Linear mixture models have proven very useful in a plethora of applications, e. g., topic modeling, clustering, and source separation.

Algorithm Clustering

Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data

no code implementations24 Apr 2018 Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong

In this work, we propose a new computational framework for large-scale SUMCOR GCCA that can easily incorporate a suite of structural regularizers which are frequently used in data analytics.

Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach

no code implementations15 Apr 2018 Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, Wing-Kin Ma

Third, the majority of the existing methods assume that there are known (or easily estimated) degradation operators applied to the SRI to form the corresponding HSI and MSI--which is hardly the case in practice.


Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications

no code implementations3 Mar 2018 Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos, Wing-Kin Ma

Perhaps a bit surprisingly, the understanding to its model identifiability---the major reason behind the interpretability in many applications such as topic mining and hyperspectral imaging---had been rather limited until recent years.

Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling

no code implementations ICML 2018 Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos

We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data.

Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors

no code implementations1 Dec 2017 Nikos Kargas, Nicholas D. Sidiropoulos, Xiao Fu

This paper shows, perhaps surprisingly, that if the joint PMF of any three variables can be estimated, then the joint PMF of all the variables can be provably recovered under relatively mild conditions.

Movie Recommendation

On Convergence of Epanechnikov Mean Shift

no code implementations20 Nov 2017 Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos

However, since the procedure involves non-smooth kernel density functions, the convergence behavior of Epanechnikov mean shift lacks theoretical support as of this writing---most of the existing analyses are based on smooth functions and thus cannot be applied to Epanechnikov Mean Shift.


On Identifiability of Nonnegative Matrix Factorization

no code implementations2 Sep 2017 Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos

In this letter, we propose a new identification criterion that guarantees the recovery of the low-rank latent factors in the nonnegative matrix factorization (NMF) model, under mild conditions.

Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm

no code implementations NeurIPS 2016 Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos

In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i. e., words that only appear (with positive probability) in one topic.

Algorithm Clustering

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

10 code implementations ICML 2017 Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong

To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN).

Clustering Dimensionality Reduction

Tensor Decomposition for Signal Processing and Machine Learning

no code implementations6 Jul 2016 Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E. Papalexakis, Christos Faloutsos

Tensors or {\em multi-way arrays} are functions of three or more indices $(i, j, k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r, c)$ for (row, column).

BIG-bench Machine Learning Collaborative Filtering +1

Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis

no code implementations31 May 2016 Xiao Fu, Kejun Huang, Mingyi Hong, Nicholas D. Sidiropoulos, Anthony Man-Cho So

Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities.

Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering

no code implementations21 May 2016 Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos

Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification.

Clustering Dimensionality Reduction

Joint Tensor Factorization and Outlying Slab Suppression with Applications

no code implementations16 Jul 2015 Xiao Fu, Kejun Huang, Wing-Kin Ma, Nicholas D. Sidiropoulos, Rasmus Bro

Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants.

Speech Separation

Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches

no code implementations7 Jul 2015 Xiao Fu, Wing-Kin Ma, José Bioucas-Dias, Tsung-Han Chan

The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.

Hyperspectral Unmixing regression

Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related

no code implementations15 Sep 2014 Xiao Fu, Wing-Kin Ma, Tsung-Han Chan, José M. Bioucas-Dias

We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noise---including its identification of the (unknown) number of endmembers---under a sufficiently low noise level.

Hyperspectral Unmixing regression

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