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09/30/2022
On the optimization and generalization of overparameterized implicit neural networks
Implicit neural networks have become increasingly attractive in the mach...
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05/16/2022
Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths
Implicit deep learning has recently become popular in the machine learni...
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10/11/2021
A global convergence theory for deep ReLU implicit networks via over-parameterization
Implicit deep learning has received increasing attention recently due to...
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05/04/2021
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Deep learning recommendation systems at scale have provided remarkable g...
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01/15/2020
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization
We propose a new randomized Bregman (block) coordinate descent (RBCD) me...
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12/16/2019
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems
In the applications of signal processing and data analytics, there is a ...
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02/04/2019
A Forest from the Trees: Generation through Neighborhoods
In this work, we propose to learn a generative model using both learned ...
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02/25/2018
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) has attracted much attention in t...
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12/22/2017
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity
Nowadays, events usually burst and are propagated online through multipl...
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03/30/2016
