Thu Dinh

activity

20182021

most citedQuantization-Guided Training for Compact TinyML Models

5 citations · 5 across the 4 of their papers we have counts for

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2 papers · 1 filter

math.OC2019

Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Networks

Thu Dinh, Jack Xin

Sparsification of neural networks is one of the effective complexity reduction methods to improve efficiency and generalizability. Binarized activation offers an additional computa…

math.OC2018

Convergence of a Relaxed Variable Splitting Method for Learning Sparse Neural Networks via $ℓ_{1}, ℓ_{0}$ , and transformed- $ℓ_{1}$ Penalties

Thu Dinh, Jack Xin

Sparsification of neural networks is one of the effective complexity reduction methods to improve efficiency and generalizability. We consider the problem of learning a one hidden…

Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Networks

Convergence of a Relaxed Variable Splitting Method for Learning Sparse Neural Networks via ℓ1​,ℓ0​, and transformed-ℓ1​ Penalties

Convergence of a Relaxed Variable Splitting Method for Learning Sparse Neural Networks via $ℓ_{1}, ℓ_{0}$ , and transformed- $ℓ_{1}$ Penalties