Tianxiang Gao

According to our database1, Tianxiang Gao authored at least 20 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
HC-MVSNet: A probability sampling-based multi-view-stereo network with hybrid cascade structure for 3D reconstruction.
Pattern Recognit. Lett., 2024

2023
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
On the optimization and generalization of overparameterized implicit neural networks.
CoRR, 2022

Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths.
CoRR, 2022

A global convergence theory for deep ReLU implicit networks via over-parameterization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation.
IEEE Trans. Medical Imaging, 2021

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems.
Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

On the Convergence of Randomized Bregman Coordinate Descent for Non-Lipschitz Composite Problems.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization.
CoRR, 2020

A Forest from the Trees: Generation through Neighborhoods.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems.
CoRR, 2019

2018
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity.
Proceedings of the Database and Expert Systems Applications, 2018

DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Extracting information from deep learning models for computational biology.
PhD thesis, 2017

2016
Degrees of Freedom in Deep Neural Networks.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Minimum-volume-regularized weighted symmetric nonnegative matrix factorization for clustering.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

2015
Drug-induced mRNA Signatures Are Enriched for the Minority of Genes that Are Highly Heritable.
Proceedings of the Biocomputing 2015: Proceedings of the Pacific Symposium, 2015

2014
Modelling relational statistics with Bayes Nets.
Mach. Learn., 2014

2012
Learning compact Markov logic networks with decision trees.
Mach. Learn., 2012

Random Regression for Bayes Nets Applied to Relational Data.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012


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