Ivor W. Tsang

Graph Cross Networks with Vertex Infomax Pooling

We propose a novel graph cross network (GXN) to achieve comprehensive fe...
10/05/2020 ∙ by Maosen Li, et al. ∙ 0 ∙ share

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Intrinsic Reward Driven Imitation Learning via Generative Model

Imitation learning in a high-dimensional environment is challenging. Mos...
06/26/2020 ∙ by Xingrui Yu, et al. ∙ 0 ∙ share

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Multi-view Alignment and Generation in CCA via Consistent Latent Encoding

Multi-view alignment, achieving one-to-one correspondence of multi-view ...
05/24/2020 ∙ by Yaxin Shi, et al. ∙ 10 ∙ share

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Secure Metric Learning via Differential Pairwise Privacy

Distance Metric Learning (DML) has drawn much attention over the last tw...
03/30/2020 ∙ by Jing Li, et al. ∙ 12 ∙ share

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Face Hallucination with Finishing Touches

Obtaining a high-quality frontal face image from a low-resolution (LR) n...
02/09/2020 ∙ by Yang Zhang, et al. ∙ 8 ∙ share

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Towards Sharper First-Order Adversary with Quantized Gradients

Despite the huge success of Deep Neural Networks (DNNs) in a wide spectr...
02/01/2020 ∙ by Zhuanghua Liu, et al. ∙ 0 ∙ share

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Stochastic Implicit Natural Gradient for Black-box Optimization

Black-box optimization is primarily important for many compute-intensive...
10/09/2019 ∙ by Yueming Lyu, et al. ∙ 0 ∙ share

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Improving Generalization via Attribute Selection on Out-of-the-box Data

Zero-shot learning (ZSL) aims to recognize unseen objects (test classes)...
07/26/2019 ∙ by Xiaofeng Xu, et al. ∙ 0 ∙ share

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Node Attribute Generation on Graphs

Graph structured data provide two-fold information: graph structures and...
07/23/2019 ∙ by Xu Chen, et al. ∙ 0 ∙ share

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Latent Adversarial Defence with Boundary-guided Generation

Deep Neural Networks (DNNs) have recently achieved great success in many...
07/16/2019 ∙ by Xiaowei Zhou, et al. ∙ 0 ∙ share

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Fast and Robust Rank Aggregation against Model Misspecification

In rank aggregation, preferences from different users are summarized int...
05/29/2019 ∙ by Yuangang Pan, et al. ∙ 0 ∙ share

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Curriculum Loss: Robust Learning and Generalization against Label Corruption

Generalization is vital important for many deep network models. It becom...
05/24/2019 ∙ by Yueming Lyu, et al. ∙ 0 ∙ share

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Efficient Batch Black-box Optimization with Deterministic Regret Bounds

In this work, we investigate black-box optimization from the perspective...
05/24/2019 ∙ by Yueming Lyu, et al. ∙ 0 ∙ share

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Marginalized Average Attentional Network for Weakly-Supervised Learning

In weakly-supervised temporal action localization, previous works have f...
05/21/2019 ∙ by Yuan Yuan, et al. ∙ 0 ∙ share

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Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation

As a kind of semantic representation of visual object descriptions, attr...
05/20/2019 ∙ by Xiaofeng Xu, et al. ∙ 0 ∙ share

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Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Learning with noisy labels, which aims to reduce expensive labors on acc...
03/06/2019 ∙ by Jiangchao Yao, et al. ∙ 2 ∙ share

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How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the most important question in weak...
01/14/2019 ∙ by Xingrui Yu, et al. ∙ 0 ∙ share

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A Survey on Multi-output Learning

Multi-output learning aims to simultaneously predict multiple outputs gi...
01/02/2019 ∙ by Donna Xu, et al. ∙ 38 ∙ share

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Privacy-preserving Stochastic Gradual Learning

It is challenging for stochastic optimizations to handle large-scale sen...
09/30/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Target-Independent Active Learning via Distribution-Splitting

To reduce the label complexity in Agnostic Active Learning (A^2 algorith...
09/28/2018 ∙ by Xiaofeng Cao, et al. ∙ 0 ∙ share

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A Structured Perspective of Volumes on Active Learning

Active Learning (AL) is a learning task that requires learners interacti...
07/24/2018 ∙ by Xiaofeng Cao, et al. ∙ 0 ∙ share

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Understanding VAEs in Fisher-Shannon Plane

In information theory, Fisher information and Shannon information (entro...
07/10/2018 ∙ by Huangjie Zheng, et al. ∙ 0 ∙ share

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Geometric Active Learning via Enclosing Ball Boundary

Active Learning (AL) requires learners to retrain the classifier with th...
05/31/2018 ∙ by Xiaofeng Cao, et al. ∙ 0 ∙ share

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Matrix Co-completion for Multi-label Classification with Missing Features and Labels

We consider a challenging multi-label classification problem where both ...
05/23/2018 ∙ by Miao Xu, et al. ∙ 0 ∙ share

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Multi-Context Label Embedding

Label embedding plays an important role in zero-shot learning. Side info...
05/03/2018 ∙ by Yaxin Shi, et al. ∙ 0 ∙ share

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Zero-shot Learning with Complementary Attributes

Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint...
04/17/2018 ∙ by Xiaofeng Xu, et al. ∙ 0 ∙ share

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VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

In this paper, we propose a simple variant of the original SVRG, called ...
02/26/2018 ∙ by Fanhua Shang, et al. ∙ 0 ∙ share

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Millionaire: A Hint-guided Approach for Crowdsourcing

Modern machine learning is migrating to the era of complex models, which...
02/26/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Degeneration in VAE: in the Light of Fisher Information Loss

Variational Autoencoder (VAE) is one of the most popular generative mode...
02/19/2018 ∙ by Huangjie Zheng, et al. ∙ 0 ∙ share

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Online Product Quantization

Approximate nearest neighbor (ANN) search has achieved great success in ...
11/29/2017 ∙ by Donna Xu, et al. ∙ 0 ∙ share

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Transfer Hashing with Privileged Information

Most existing learning to hash methods assume that there are sufficient ...
05/13/2016 ∙ by Joey Tianyi Zhou, et al. ∙ 0 ∙ share

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A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation

Many scientific datasets are of high dimension, and the analysis usually...
12/09/2015 ∙ by Qi Mao, et al. ∙ 0 ∙ share

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Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals

Matching Pursuit LASSIn Part I TanPMLPart1, a Matching Pursuit LASSO (MP...
02/20/2013 ∙ by Mingkui Tan, et al. ∙ 0 ∙ share

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Parameter-Free Spectral Kernel Learning

Due to the growing ubiquity of unlabeled data, learning with unlabeled d...
03/15/2012 ∙ by Qi Mao, et al. ∙ 0 ∙ share

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Hierarchical Maximum Margin Learning for Multi-Class Classification

Due to myriads of classes, designing accurate and efficient classifiers ...
02/14/2012 ∙ by Jian-Bo Yang, et al. ∙ 0 ∙ share

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Efficient Optimization of Performance Measures by Classifier Adaptation

In practical applications, machine learning algorithms are often needed ...
12/04/2010 ∙ by Nan Li, et al. ∙ 0 ∙ share

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Ivor W. Tsang

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