Search Results for author: Qi Lyu

Found 8 papers, 1 papers with code

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

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

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.

Deep Learning for Genomics: A Concise Overview

no code implementations2 Feb 2018 Tianwei Yue, Yuanxin Wang, Longxiang Zhang, Chunming Gu, Haoru Xue, Wenping Wang, Qi Lyu, Yujie Dun

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines.

WristAuthen: A Dynamic Time Wrapping Approach for User Authentication by Hand-Interaction through Wrist-Worn Devices

no code implementations22 Oct 2017 Qi Lyu, Zhifeng Kong, Chao Shen, Tianwei Yue

This paper presents a novel user authentication system through wrist-worn devices by analyzing the interaction behavior with users, which is both accurate and efficient for future usage.

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