5 citations · 5 across the 4 of their papers we have counts for
6 papers
Quantization-Guided Training for Compact TinyML Models
Sedigh Ghamari, Koray Ozcan, Thu Dinh +4
We propose a Quantization Guided Training (QGT) method to guide DNN training towards optimized low-bit-precision targets and reach extreme compression levels below 8-bit precision.…
Subtensor Quantization for Mobilenets
Thu Dinh, Andrey Melnikov, Vasilios Daskalopoulos +1
Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference. However, not all DNN designs are frien…
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets
Thu Dinh, Bao Wang, Andrea L. Bertozzi +1
Deep neural nets (DNNs) compression is crucial for adaptation to mobile devices. Though many successful algorithms exist to compress naturally trained DNNs, developing efficient an…
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…
Convergence of a Relaxed Variable Splitting Method for Learning Sparse Neural Networks via , and transformed- 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…
Enhanced Diffusivity in Perturbed Senile Reinforced Random Walk Models
Thu Dinh, Jack Xin
We consider diffusivity of random walks with transition probabilities depending on the number of consecutive traversals of the last traversed edge, the so called senile reinforced…