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11/14/2022
Heterogeneous Graph Sparsification for Efficient Representation Learning
Graph sparsification is a powerful tool to approximate an arbitrary grap...
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10/12/2022
Auto-Encoding Goodness of Fit
For generative autoencoders to learn a meaningful latent representation ...
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08/07/2022
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Transformers are considered one of the most important deep learning mode...
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09/13/2021
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search
Molecular similarity search has been widely used in drug discovery to id...
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04/12/2021
An Efficient Algorithm for Deep Stochastic Contextual Bandits
In stochastic contextual bandit (SCB) problems, an agent selects an acti...
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03/07/2021
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods
Stochastically controlled stochastic gradient (SCSG) methods have been p...
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01/18/2021
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Time series forecasting is an extensively studied subject in statistics,...
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12/31/2020
Federated Nonconvex Sparse Learning
Nonconvex sparse learning plays an essential role in many areas, such as...
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11/02/2020
Asynchronous Parallel Stochastic Quasi-Newton Methods
Although first-order stochastic algorithms, such as stochastic gradient ...
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09/14/2020
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning
Sparse learning is a very important tool for mining useful information a...
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09/14/2020
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data
Federated learning allows loads of edge computing devices to collaborati...
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08/28/2020
MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks
Deep learning or deep neural networks (DNNs) have nowadays enabled high ...
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08/11/2020
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
The Alternating Direction Method of Multipliers (ADMM) and its distribut...
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08/02/2019
Calibrating the Learning Rate for Adaptive Gradient Methods to Improve Generalization Performance
Although adaptive gradient methods (AGMs) have fast speed in training de...
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11/14/2018
Communication-Optimal Distributed Dynamic Graph Clustering
We consider the problem of clustering graph nodes over large-scale dynam...
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11/11/2018
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
Knowledge graph embedding has been an active research topic for knowledg...
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02/14/2018
Edge Attention-based Multi-Relational Graph Convolutional Networks
Graph convolutional network (GCN) is generalization of convolutional neu...
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12/18/2017
A Survey on Multi-View Clustering
With the fast development of information technology, especially the popu...
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08/22/2017
VIGAN: Missing View Imputation with Generative Adversarial Networks
In an era when big data are becoming the norm, there is less concern wit...
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12/08/2016