Search Results for author: Quan Li

Found 6 papers, 0 papers with code

Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference

no code implementations10 Jan 2024 Quan Li, Shixiong Jing, Lingwei Chen

Experiments on social media datasets demonstrate the state-of-the-art performance of our model on attribute inferences with considerably fewer labeled texts.

Attribute Few-Shot Learning +3

Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective

no code implementations18 Sep 2023 Laixin Xie, Yang Ouyang, Longfei Chen, Ziming Wu, Quan Li

These approaches rely on the observed data to estimate the missing values and therefore encounter three main shortcomings in imputation, including the need for different imputation methods for various missing data mechanisms, heavy dependence on the assumption of data distribution, and potential introduction of bias.

Contrastive Learning Imputation

Inspecting the Process of Bank Credit Rating via Visual Analytics

no code implementations6 Aug 2021 Qiangqiang Liu, Quan Li, Zhihua Zhu, Tangzhi Ye, Xiaojuan Ma

We propose RatingVis to assist experts in exploring and comparing different bank credit rating schemes.

Management

EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos

no code implementations29 Jul 2019 Haipeng Zeng, Xingbo Wang, Aoyu Wu, Yong Wang, Quan Li, Alex Endert, Huamin Qu

Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution.

Clustering Sentence

Privileged Features Distillation at Taobao Recommendations

no code implementations11 Jul 2019 Chen Xu, Quan Li, Junfeng Ge, Jinyang Gao, Xiaoyong Yang, Changhua Pei, Fei Sun, Jian Wu, Hanxiao Sun, Wenwu Ou

To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available.

Matrix Completion from $O(n)$ Samples in Linear Time

no code implementations8 Feb 2017 David Gamarnik, Quan Li, Hongyi Zhang

Under a certain incoherence assumption on $M$ and for the case when both the rank and the condition number of $M$ are bounded, it was shown in \cite{CandesRecht2009, CandesTao2010, keshavan2010, Recht2011, Jain2012, Hardt2014} that $M$ can be recovered exactly or approximately (depending on some trade-off between accuracy and computational complexity) using $O(n \, \text{poly}(\log n))$ samples in super-linear time $O(n^{a} \, \text{poly}(\log n))$ for some constant $a \geq 1$.

Matrix Completion

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