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Improving Generalization and Stability of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are one of the most popular tools...
02/11/2019 ∙ by Hoang Thanh-Tung, et al. ∙ 26 ∙ share

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Review, Analyze, and Design a Comprehensive Deep Reinforcement Learning Framework

Reinforcement learning (RL) has emerged as a standard approach for build...
02/27/2020 ∙ by Ngoc Duy Nguyen, et al. ∙ 24 ∙ share

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Distributionally Robust Bayesian Quadrature Optimization

Bayesian quadrature optimization (BQO) maximizes the expectation of an e...
01/19/2020 ∙ by Thanh Tang Nguyen, et al. ∙ 23 ∙ share

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Spoof Face Detection Via Semi-Supervised Adversarial Training

Face spoofing causes severe security threats in face recognition systems...
05/22/2020 ∙ by Chengwei Chen, et al. ∙ 17 ∙ share

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Self-Assttentive Associative Memory

Heretofore, neural networks with external memory are restricted to singl...
02/10/2020 ∙ by Hung Le, et al. ∙ 13 ∙ share

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Self-Attentive Associative Memory

Heretofore, neural networks with external memory are restricted to singl...
02/10/2020 ∙ by Hung Le, et al. ∙ 12 ∙ share

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Interpretable and Efficient Heterogeneous Graph Convolutional Network

Graph Convolutional Network (GCN) has achieved extraordinary success in ...
05/27/2020 ∙ by Yaming Yang, et al. ∙ 10 ∙ share

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SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structural Similarity

Deeper convolutional neural networks provide more capacity to approximat...
06/24/2018 ∙ by Ahmed Abobakr, et al. ∙ 8 ∙ share

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Bayesian functional optimisation with shape prior

Real world experiments are expensive, and thus it is important to reach ...
09/19/2018 ∙ by Pratibha Vellanki, et al. ∙ 8 ∙ share

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Towards effective AI-powered agile project management

The rise of Artificial intelligence (AI) has the potential to significan...
12/27/2018 ∙ by Hoa Khanh Dam, et al. ∙ 8 ∙ share

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PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study

This paper introduces a newly collected and novel dataset (StereoMSI) fo...
04/01/2019 ∙ by Mehrdad Shoeiby, et al. ∙ 8 ∙ share

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Theory and Evaluation Metrics for Learning Disentangled Representations

We make two theoretical contributions to disentanglement learning by (a)...
08/26/2019 ∙ by Kien Do, et al. ∙ 8 ∙ share

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Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network

This paper introduces a novel method to simultaneously super-resolve and...
09/05/2019 ∙ by Mehrdad Shoeiby, et al. ∙ 7 ∙ share

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Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

Appearance features have been widely used in video anomaly detection eve...
03/08/2019 ∙ by Romero Morais, et al. ∙ 6 ∙ share

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Explicit Facial Expression Transfer via Fine-Grained Semantic Representations

Facial expression transfer between two unpaired images is a challenging ...
09/06/2019 ∙ by Zhiwen Shao, et al. ∙ 6 ∙ share

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A Visual Communication Map for Multi-Agent Deep Reinforcement Learning

Multi-agent learning distinctly poses significant challenges in the effo...
02/27/2020 ∙ by Ngoc Duy Nguyen, et al. ∙ 6 ∙ share

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Learning to Remember More with Less Memorization

Memory-augmented neural networks consisting of a neural controller and a...
01/05/2019 ∙ by Hung Le, et al. ∙ 4 ∙ share

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Kernel-Free Image Deblurring with a Pair of Blurred/Noisy Images

Complex blur like the mixup of space-variant and space-invariant blur, w...
03/26/2019 ∙ by Chunzhi Gu, et al. ∙ 4 ∙ share

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Learning to Reason with Relational Video Representation for Question Answering

How does machine learn to reason about the content of a video in answeri...
07/10/2019 ∙ by Thao Minh Le, et al. ∙ 4 ∙ share

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Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling

Scientific experiments are usually expensive due to complex experimental...
02/26/2020 ∙ by Cheng Li, et al. ∙ 4 ∙ share

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Incorporating Expert Prior in Bayesian Optimisation via Space Warping

Bayesian optimisation is a well-known sample-efficient method for the op...
03/27/2020 ∙ by Anil Ramachandran, et al. ∙ 3 ∙ share

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Multi-Agent Deep Reinforcement Learning with Human Strategies

Deep learning has enabled traditional reinforcement learning methods to ...
06/12/2018 ∙ by Thanh Nguyen, et al. ∙ 2 ∙ share

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On catastrophic forgetting and mode collapse in Generative Adversarial Networks

Generative Adversarial Networks (GAN) are one of the most prominent tool...
07/11/2018 ∙ by Hoang Thanh-Tung, et al. ∙ 2 ∙ share

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Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics

Device fingerprints like sensor pattern noise (SPN) are widely used for ...
04/22/2020 ∙ by Yijun Quan, et al. ∙ 2 ∙ share

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HDF: Hybrid Deep Features for Scene Image Representation

Nowadays it is prevalent to take features extracted from pre-trained dee...
03/22/2020 ∙ by Chiranjibi Sitaula, et al. ∙ 1 ∙ share

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Automatic feature learning for vulnerability prediction

Code flaws or vulnerabilities are prevalent in software systems and can ...
08/08/2017 ∙ by Hoa Khanh Dam, et al. ∙ 0 ∙ share

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KGAN: How to Break The Minimax Game in GAN

Generative Adversarial Networks (GANs) were intuitively and attractively...
11/06/2017 ∙ by Trung Le, et al. ∙ 0 ∙ share

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Creating a Social Brain for Cooperative Connected Autonomous Vehicles: Issues and Challenges

The connected autonomous vehicle has been often touted as a technology t...
10/02/2017 ∙ by Seng W. Loke, et al. ∙ 0 ∙ share

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Geometric Enclosing Networks

Training model to generate data has increasingly attracted research atte...
08/16/2017 ∙ by Trung Le, et al. ∙ 0 ∙ share

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Multi-Generator Generative Adversarial Nets

We propose a new approach to train the Generative Adversarial Nets (GANs...
08/08/2017 ∙ by Quan Hoang, et al. ∙ 0 ∙ share

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Analogical-based Bayesian Optimization

Some real-world problems revolve to solve the optimization problem _x∈Xf...
09/19/2017 ∙ by Trung Le, et al. ∙ 0 ∙ share

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Dual Discriminator Generative Adversarial Nets

We propose in this paper a novel approach to tackle the problem of mode ...
09/12/2017 ∙ by Tu Dinh Nguyen, et al. ∙ 0 ∙ share

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Faster Training of Very Deep Networks Via p-Norm Gates

A major contributing factor to the recent advances in deep neural networ...
08/11/2016 ∙ by Trang Pham, et al. ∙ 0 ∙ share

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Statistical Latent Space Approach for Mixed Data Modelling and Applications

The analysis of mixed data has been raising challenges in statistics and...
08/18/2017 ∙ by Tu Dinh Nguyen, et al. ∙ 0 ∙ share

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Wind ramp event prediction with parallelized Gradient Boosted Regression Trees

Accurate prediction of wind ramp events is critical for ensuring the rel...
10/17/2016 ∙ by Saurav Gupta, et al. ∙ 0 ∙ share

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A Random Finite Set Model for Data Clustering

The goal of data clustering is to partition data points into groups to m...
03/14/2017 ∙ by Dinh Phung, et al. ∙ 0 ∙ share

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Robust artificial neural networks and outlier detection. Technical report

Large outliers break down linear and nonlinear regression models. Robust...
10/02/2011 ∙ by Gleb Beliakov, et al. ∙ 0 ∙ share

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Stabilizing Linear Prediction Models using Autoencoder

To date, the instability of prognostic predictors in a sparse high dimen...
09/28/2016 ∙ by Shivapratap Gopakumar, et al. ∙ 0 ∙ share

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A deep learning model for estimating story points

Although there has been substantial research in software analytics for e...
09/02/2016 ∙ by Morakot Choetkiertikul, et al. ∙ 0 ∙ share

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A deep language model for software code

Existing language models such as n-grams for software code often fail to...
08/09/2016 ∙ by Hoa Khanh Dam, et al. ∙ 0 ∙ share

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DeepSoft: A vision for a deep model of software

Although software analytics has experienced rapid growth as a research a...
07/30/2016 ∙ by Hoa Khanh Dam, et al. ∙ 0 ∙ share

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Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data

Preterm births occur at an alarming rate of 10-15 risk of infant mortali...
07/28/2016 ∙ by Truyen Tran, et al. ∙ 0 ∙ share

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An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments

Accurate prediction of suicide risk in mental health patients remains an...
05/03/2016 ∙ by Thuong Nguyen, et al. ∙ 0 ∙ share

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Approximation Vector Machines for Large-scale Online Learning

One of the most challenging problems in kernel online learning is to bou...
04/22/2016 ∙ by Trung Le, et al. ∙ 0 ∙ share

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Weakly monotone averaging functions

Monotonicity with respect to all arguments is fundamental to the definit...
08/02/2014 ∙ by Tim Wilkin, et al. ∙ 0 ∙ share

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Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori (MAP) assignment for general structure Markov r...
07/22/2014 ∙ by Truyen Tran, et al. ∙ 0 ∙ share

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Collaborative filtering via sparse Markov random fields

Recommender systems play a central role in providing individualized acce...
02/09/2016 ∙ by Truyen Tran, et al. ∙ 0 ∙ share

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A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation

Recent advances of 3D acquisition devices have enabled large-scale acqui...
10/19/2016 ∙ by Duc Thanh Nguyen, et al. ∙ 0 ∙ share

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Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks

In this article, we take one step toward understanding the learning beha...
09/19/2016 ∙ by Masoud Abdi, et al. ∙ 0 ∙ share

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Mixed-Variate Restricted Boltzmann Machines

Modern datasets are becoming heterogeneous. To this end, we present in t...
08/06/2014 ∙ by Truyen Tran, et al. ∙ 0 ∙ share

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