
-
Attentive Social Recommendation: Towards User And Item Diversities
Social recommendation system is to predict unobserved user-item rating v...
read it
-
Parameter-Free Style Projection for Arbitrary Style Transfer
Arbitrary image style transfer is a challenging task which aims to styli...
read it
-
Ultrafast Photorealistic Style Transfer via Neural Architecture Search
The key challenge in photorealistic style transfer is that an algorithm ...
read it
-
SecureGBM: Secure Multi-Party Gradient Boosting
Federated machine learning systems have been widely used to facilitate t...
read it
-
Towards Making Deep Transfer Learning Never Hurt
Transfer learning have been frequently used to improve deep neural netwo...
read it
-
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
The problem of explaining deep learning models, and model predictions ge...
read it
-
Fast Universal Style Transfer for Artistic and Photorealistic Rendering
Universal style transfer is an image editing task that renders an input ...
read it
-
AGAN: Towards Automated Design of Generative Adversarial Networks
Recent progress in Generative Adversarial Networks (GANs) has shown prom...
read it
-
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...
read it
-
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...
read it
-
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
We present a novel method of compression of deep Convolutional Neural Ne...
read it
-
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity
Regularization of Deep Neural Networks (DNNs) for the sake of improving ...
read it
-
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks
Transfer learning through fine-tuning a pre-trained neural network with ...
read it
-
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...
read it
-
Instance-based Deep Transfer Learning
Deep transfer learning has acquired significant research interest. It ma...
read it
-
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Deep learning models learn to fit training data while they are highly ex...
read it
-
Understanding Actors and Evaluating Personae with Gaussian Embeddings
Understanding narrative content has become an increasingly popular topic...
read it
-
Discriminatory Transfer
We observe standard transfer learning can improve prediction accuracies ...
read it
-
On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics
In cheminformatics, compound-target binding profiles has been a main sou...
read it