Search Results for author:
Found 18 papers, 7 papers with code
Exploring Pathological Speech Quality Assessment with ASR-Powered Wav2Vec2 in Data-Scarce Context
Automatic speech quality assessment has raised more attention as an alternative or support to traditional perceptual clinical evaluation.
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation
Additionally, we propose minimizing class-aware Higher-order Moment Matching (HMM) to align the corresponding class regions on the source and target domains.
Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
Federated Recommendation (FedRec) systems have emerged as a solution to safeguard users' data in response to growing regulatory concerns.
DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition
The task of Visual Relationship Recognition (VRR) aims to identify relationships between two interacting objects in an image and is particularly challenging due to the widely-spread and highly imbalanced distribution of <subject, relation, object> triplets.
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning
To address this, we propose the Gradient Projection Class-Prototype Conditional Diffusion Model (GPPDM), a GR-based approach for continual learning that enhances image quality in generators and thus reduces the CF in classifiers.
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach
We propose the Kuramoto Graph Neural Network (KuramotoGNN), a novel class of continuous-depth graph neural networks (GNNs) that employs the Kuramoto model to mitigate the over-smoothing phenomenon, in which node features in GNNs become indistinguishable as the number of layers increases.
p-Laplacian Transformer
$p$-Laplacian regularization, rooted in graph and image signal processing, introduces a parameter $p$ to control the regularization effect on these data.
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions
Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy.
Federated Learning for ASR based on Wav2vec 2.0
This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2. 0 model pre-trained by self supervision.
Sparse*BERT: Sparse Models Generalize To New tasks and Domains
Our experimentation shows that models that are pruned during pretraining using general domain masked language models can transfer to novel domains and tasks without extensive hyperparameter exploration or specialized approaches.
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
We perform an in-depth study of the accuracy-compression trade-off for unstructured weight pruning of BERT models.
On Label Shift in Domain Adaptation via Wasserstein Distance
We study the label shift problem between the source and target domains in general domain adaptation (DA) settings.
SP-GPT2: Semantics Improvement in Vietnamese Poetry Generation
Both automatic and human evaluation demonstrated that our approach can generate poems that have better cohesion without losing the quality due to additional loss.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning.
Imitation Learning
Multi-Source Unsupervised Domain Adaptation
+1
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security.
Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation
We compared the performance between the auto-detection system and the human eye.
