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07/04/2023
Random Walk on Multiple Networks
Random Walk is a basic algorithm to explore the structure of networks, w...
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12/20/2022
Temporal Output Discrepancy for Loss Estimation-based Active Learning
While deep learning succeeds in a wide range of tasks, it highly depends...
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07/19/2022
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
As deep learning models have gradually become the main workhorse of time...
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07/29/2021
Semi-Supervised Active Learning with Temporal Output Discrepancy
While deep learning succeeds in a wide range of tasks, it highly depends...
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11/09/2020
Attentive Social Recommendation: Towards User And Item Diversities
Social recommendation system is to predict unobserved user-item rating v...
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03/17/2020
Parameter-Free Style Projection for Arbitrary Style Transfer
Arbitrary image style transfer is a challenging task which aims to styli...
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12/05/2019
Ultrafast Photorealistic Style Transfer via Neural Architecture Search
The key challenge in photorealistic style transfer is that an algorithm ...
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11/27/2019
SecureGBM: Secure Multi-Party Gradient Boosting
Federated machine learning systems have been widely used to facilitate t...
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11/18/2019
Towards Making Deep Transfer Learning Never Hurt
Transfer learning have been frequently used to improve deep neural netwo...
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09/10/2019
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
The problem of explaining deep learning models, and model predictions ge...
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07/06/2019
Fast Universal Style Transfer for Artistic and Photorealistic Rendering
Universal style transfer is an image editing task that renders an input ...
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06/25/2019
AGAN: Towards Automated Design of Generative Adversarial Networks
Recent progress in Generative Adversarial Networks (GANs) has shown prom...
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06/18/2019
The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation
The randomness in Stochastic Gradient Descent (SGD) is considered to pla...
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06/06/2019
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks
Neural Architecture Search (NAS) has been widely studied for designing d...
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02/08/2019
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
We present a novel method of compression of deep Convolutional Neural Ne...
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02/03/2019
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity
Regularization of Deep Neural Networks (DNNs) for the sake of improving ...
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01/26/2019
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks
Transfer learning through fine-tuning a pre-trained neural network with ...
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01/18/2019
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent
We interpret the variational inference of the Stochastic Gradient Descen...
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09/08/2018
Instance-based Deep Transfer Learning
Deep transfer learning has acquired significant research interest. It ma...
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09/01/2018
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Deep learning models learn to fit training data while they are highly ex...
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04/06/2018
Understanding Actors and Evaluating Personae with Gaussian Embeddings
Understanding narrative content has become an increasingly popular topic...
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07/03/2017
Discriminatory Transfer
We observe standard transfer learning can improve prediction accuracies ...
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04/03/2017
