56 citations · 106 across the 46 of their papers we have counts for
7 papers · 1 filter
Pairing Regularization for Mitigating Many-to-One Collapse in GANs
Kuan-Yu Lin, Yu-Chih Huang, Tie Liu
Mode collapse remains a fundamental challenge in training generative adversarial networks (GANs). While existing works have primarily focused on inter-mode collapse, such as mode d…
On optimal solutions of classical and sliced Wasserstein GANs with non-Gaussian data
Yu-Jui Huang, Hsin-Hua Shen, Yu-Chih Huang +2
The generative adversarial network (GAN) aims to approximate an unknown distribution via a parameterized neural network (NN). While GANs have been widely applied in reinforcement a…
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits
Kuan-Ta Li, Ping-Chun Hsieh, Yu-Chih Huang
The piecewise-stationary bandit problem is an important variant of the multi-armed bandit problem that further considers abrupt changes in the reward distributions. The main theme…
Age Aware Scheduling for Differentially-Private Federated Learning
Kuan-Yu Lin, Hsuan-Yin Lin, Yu-Pin Hsu +1
This paper explores differentially-private federated learning (FL) across time-varying databases, delving into a nuanced three-way tradeoff involving age, accuracy, and differentia…
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
This paper introduces CO3 -- an algorithm for communication-efficient federated Deep Neural Network (DNN) training. CO3 takes its name from three processing applied which reduce th…
Convert, compress, correct: Three steps toward communication-efficient DNN training
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
In this paper, we introduce a novel algorithm, , for communication-efficiency distributed Deep Neural Network (DNN) training. is a joint training/com…
DNN gradient lossless compression: Can GenNorm be the answer?
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
In this paper, the problem of optimal gradient lossless compression in Deep Neural Network (DNN) training is considered. Gradient compression is relevant in many distributed DNN tr…