Tianxiang Gao | DeepAI

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Featured Co-authors

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Hongyang Gao
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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...

0 Tianxiang Gao, et al. ∙

∙ 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...

0 Tianxiang Gao, et al. ∙

∙ 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...

0 Tianxiang Gao, et al. ∙

∙ 05/04/2021

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

Deep learning recommendation systems at scale have provided remarkable g...

15 Xiaocong Du, et al. ∙

∙ 01/15/2020

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization

We propose a new randomized Bregman (block) coordinate descent (RBCD) me...

0 Tianxiang Gao, et al. ∙

∙ 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 ...

0 Tianxiang Gao, et al. ∙

∙ 02/04/2019

A Forest from the Trees: Generation through Neighborhoods

In this work, we propose to learn a generative model using both learned ...

0 Yang Li, et al. ∙

∙ 02/25/2018

DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) has attracted much attention in t...

0 Tianxiang Gao, et al. ∙

∙ 12/22/2017

DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

Nowadays, events usually burst and are propagated online through multipl...

0 Tianxiang Gao, et al. ∙

∙ 03/30/2016

Degrees of Freedom in Deep Neural Networks

In this paper, we explore degrees of freedom in deep sigmoidal neural ne...

0 Tianxiang Gao, et al. ∙

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