Search Results for author:
Found 11 papers, 4 papers with code
Carrier Frequency Offset Estimation for OCDM with Null Subchirps
In this paper, we investigate the carrier frequency offset (CFO) identifiability problem in orthogonal chirp division multiplexing (OCDM) systems.
Precheck Sequence Based False Base Station Detection During Handover: A Physical Layer Security Scheme
The source BS compares the uploaded signal power and shifts UE to another BS that can provide the strongest signal.
Rethinking the Image Feature Biases Exhibited by Deep CNN Models
From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature.
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(10-keyword Speech Commands dataset metric)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression
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.
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression
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 expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error.
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
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 to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)
Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance.
Variational Quantum Circuits for Deep Reinforcement Learning
To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation.
