303 citations · 483 across the 11 of their papers we have counts for
12 papers
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi, Jun Du, Sabato Marco Siniscalchi +2
In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work…
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi, Jun Du, Sabato Marco Siniscalchi +2
In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expecte…
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +2
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based…
Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction
Yu Han, Mengyuan Li, Shi Jin +2
This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems…
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +4
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown…
Channel Estimation for Extremely Large-Scale Massive MIMO Systems
Yu Han, Shi Jin, Chao-Kai Wen +1
Extremely large-scale massive multiple-input multiple-output (MIMO) has shown considerable potential in future mobile communications. However, the use of extremely large aperture a…