In recent years, graph neural networks (GNN) have achieved significant
d...
The remarkable performance of deep Convolutional neural networks (CNNs) ...
For best performance, today's semantic segmentation methods use large an...
Traffic data chronically suffer from missing and corruption, leading to
...
The exposure sequence is being actively studied for user interest modeli...
In this paper, Particle-in-Cell algorithms for the Vlasov-Poisson system...
We propose a general Variational Embedding Learning Framework (VELF) for...
Federated learning is a powerful distributed learning scheme that allows...
The rapid advances in deep generative models over the past years have le...
Federated learning facilitates learning across clients without transferr...
We present NaturalCC, an efficient and extensible toolkit to bridge the ...
Recently, learning-based image synthesis has enabled to generate
high-re...
This paper focuses on network pruning for image retrieval acceleration.
...
Today's success of state of the art methods for semantic segmentation is...
Improving the performance of click-through rate (CTR) prediction remains...
Existing methods usually utilize pre-defined criterions, such as p-norm,...
Deep neural networks (DNNs) have dramatically achieved great success on ...
Previous works utilized "smaller-norm-less-important" criterion to prune...
This paper proposed a Progressive Soft Filter Pruning method (PSFP) to p...
This paper proposed a Soft Filter Pruning (SFP) method to accelerate the...
Recent advances in Deep Learning and probabilistic modeling have led to
...
This paper describes a quantum programming environment, named Q|SI〉.
It ...
Contextual information is crucial for semantic segmentation. However, fi...
Designing an e-commerce recommender system that serves hundreds of milli...
We present LADDER, the first deep reinforcement learning agent that can
...
We propose a novel superpixel-based multi-view convolutional neural netw...