This paper investigates the in-context learning abilities of the Whisper...
In this work, we introduce a “score-based assessment” framework for
esti...
We propose a multi-dimensional structured state space (S4) approach to s...
In this work, we devise a parameter-efficient solution to bring differen...
This paper presents a parameter-efficient learning (PEL) to develop a
lo...
In this work, we explore Parameter-Efficient-Learning (PEL) techniques t...
In this work, we propose a new parameter-efficient learning framework ba...
Transfer learning (TL) approaches have shown promising results when hand...
We propose a quantum kernel learning (QKL) framework to address the inhe...
This study addresses the speech enhancement (SE) task within the causal
...
Recently, quantum classifiers have been known to be vulnerable to advers...
We propose an ensemble learning framework with Poisson sub-sampling to
e...
Differential privacy (DP) is one data protection avenue to safeguard use...
The noisy intermediate-scale quantum (NISQ) devices enable the implement...
Current top-notch deep learning (DL) based vision models are primarily b...
This work focuses on designing low complexity hybrid tensor networks by
...
In this paper, we propose two techniques, namely joint modeling and data...
The rapid development of quantum computing has demonstrated many unique
...
In this work, we aim to enhance the system robustness of end-to-end auto...
Audio-only-based wake word spotting (WWS) is challenging under noisy
con...
Deep Reinforcement Learning (DRL) has demonstrated great potentials in
s...
We propose a variational Bayesian (VB) approach to learning distribution...
In this study, we propose a novel adversarial reprogramming (AR) approac...
The advent of noisy intermediate-scale quantum (NISQ) computers raises a...
We propose a novel neural model compression strategy combining data
augm...
Learning to classify time series with limited data is a practical yet
ch...
Automatically generating medical reports for retinal images is one of th...
We propose using an adversarial autoencoder (AAE) to replace generative
...
Deep reinforcement learning (DRL) has demonstrated impressive performanc...
End-to-end automatic speech recognition (ASR) systems are increasingly
p...
To improve device robustness, a highly desirable key feature of a compet...
In this work, we propose an AI-based method that intends to improve the
...
We propose a novel decentralized feature extraction approach in federate...
This paper investigates different trade-offs between the number of model...
Single image deraining is a crucial problem because rain severely degene...
In this technical report, we present a joint effort of four groups, name...
Recent studies have highlighted adversarial examples as ubiquitous threa...
Single image dehazing is the ill-posed two-dimensional signal reconstruc...
Recent deep neural networks based techniques, especially those equipped ...
We propose a tensor-to-vector regression approach to multi-channel speec...
NeuroEvolution is one of the most competitive evolutionary learning
fram...
Distributed automatic speech recognition (ASR) requires to aggregate out...
Applying Machine Learning (ML) techniques to design and optimize compute...
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal diseas...
Discovering and exploiting the causality in deep neural networks (DNNs) ...
Discovering and exploiting the causality in deep neural networks (DNNs) ...