Dachao Lin

Orcid: 0000-0001-6560-8816

According to our database1, Dachao Lin authored at least 19 papers between 2010 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
LocMoE+: Enhanced Router with Token Feature Awareness for Efficient LLM Pre-Training.
CoRR, 2024

2023
Towards explicit superlinear convergence rate for SR1.
Math. Program., May, 2023

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods.
J. Mach. Learn. Res., 2022

On the Convergence of Policy in Unregularized Policy Mirror Descent.
CoRR, 2022

Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer.
CoRR, 2022

On the landscape of one-hidden-layer sparse networks and beyond.
Artif. Intell., 2022

On Non-local Convergence Analysis of Deep Linear Networks.
Proceedings of the International Conference on Machine Learning, 2022

2021
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations.
CoRR, 2021

Directional Convergence Analysis under Spherically Symmetric Distribution.
CoRR, 2021

Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent.
CoRR, 2021

Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Landscape of Sparse Linear Network: A Brief Investigation.
CoRR, 2020

Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond.
CoRR, 2020

2019
Towards Understanding the Importance of Noise in Training Neural Networks.
CoRR, 2019

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks.
CoRR, 2019

Toward Understanding the Importance of Noise in Training Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

2010
Application research of comprehensive evaluation based on the theory of unascertainty measure.
Proceedings of the Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010


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