Search Results for author: A. K. Qin

Found 17 papers, 4 papers with code

ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning

no code implementations29 Oct 2021 Maoguo Gong, Yuan Gao, Yue Wu, A. K. Qin

Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution.

Learning Enhanced Optimisation for Routing Problems

no code implementations17 Sep 2021 Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar, Babak Abbasi, A. K. Qin

However, there is still a substantial gap in solution quality between machine learning and operations research algorithms.

Evolutionary Ensemble Learning for Multivariate Time Series Prediction

no code implementations22 Aug 2021 Hui Song, A. K. Qin, Flora D. Salim

In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs).

Ensemble Learning Time Series +1

AdvDrop: Adversarial Attack to DNNs by Dropping Information

1 code implementation ICCV 2021 Ranjie Duan, Yuefeng Chen, Dantong Niu, Yun Yang, A. K. Qin, Yuan He

Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e. g. cartoon.

Adversarial Attack Adversarial Robustness

Multi-Party Dual Learning

no code implementations14 Apr 2021 Maoguo Gong, Yuan Gao, Yu Xie, A. K. Qin, Ke Pan, Yew-Soon Ong

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.

Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework

no code implementations14 Apr 2021 Yuan Gao, Jiawei Li, Maoguo Gong, Yu Xie, A. K. Qin

Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem.

Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

1 code implementation CVPR 2021 Ranjie Duan, Xiaofeng Mao, A. K. Qin, Yun Yang, Yuefeng Chen, Shaokai Ye, Yuan He

Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario.

Adversarial Attack

Learning Vehicle Routing Problems using Policy Optimisation

no code implementations24 Dec 2020 Nasrin Sultana, Jeffrey Chan, A. K. Qin, Tabinda Sarwar

In our evaluation, we experimentally illustrate that the model produces state-of-the-art performance on variants of Vehicle Routing problems such as Capacitated Vehicle Routing Problem (CVRP), Multiple Routing with Fixed Fleet Problems (MRPFF) and Travelling Salesman problem.

Learning to Optimise General TSP Instances

no code implementations23 Oct 2020 Nasrin Sultana, Jeffrey Chan, A. K. Qin, Tabinda Sarwar

In recent years, learning to optimise approaches have shown success in solving TSP problems.

Meta-Learning

Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations

1 code implementation12 Oct 2020 Wenfeng Liu, Maoguo Gong, Zedong Tang, A. K. Qin

To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer's readout to form a global context-aware node representation.

Classification General Classification +2

A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization

no code implementations2 Jul 2020 Boyu Zhang, A. K. Qin, Hong Pan, Timos Sellis

The training set is used for training the model while the validation set is used to estimate the generalization performance of the trained model as the training proceeds to avoid over-fitting.

Transfer Learning

DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition

no code implementations13 Jun 2020 Ziming Liu, Guangyu Gao, A. K. Qin, Jinyang Li

Finally, the DTG-Net is evaluated in two ways: (i) the self-supervised DTG-Net to pre-train the supervised action recognition models with only unlabeled videos; (ii) the supervised DTG-Net to be jointly trained with the supervised action networks in an end-to-end way.

Action Recognition Image Classification +1

Location-Centered House Price Prediction: A Multi-Task Learning Approach

no code implementations7 Jan 2019 Guangliang Gao, Zhifeng Bao, Jie Cao, A. K. Qin, Timos Sellis, Fellow, IEEE, Zhiang Wu

Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.

Multi-Task Learning

Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results

no code implementations12 Jun 2017 Bingshui Da, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Zexuan Zhu, Chuan-Kang Ting, Ke Tang, Xin Yao

In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously.

Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

no code implementations8 Jun 2017 Yuan Yuan, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Bingshui Da, Qingfu Zhang, Kay Chen Tan, Yaochu Jin, Hisao Ishibuchi

In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.

Multiobjective Optimization

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