Search Results for author:
Found 16 papers, 6 papers with code
Proactive Detection of Voice Cloning with Localized Watermarking
In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning.
Seamless: Multilingual Expressive and Streaming Speech Translation
In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion.
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
Ranked #1 on
Machine Translation
on flores95-devtest eng-X
Automatic Speech Recognition
Speech-to-Speech Translation
+3
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction
Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems.
On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning
The UK anti-fraud charity Fraud Advisory Panel (FAP) in their review of 2016 estimates business costs of fraud at 144 billion, and its individual counterpart at 9. 7 billion.
Understanding Public Opinion on Using Hydroxychloroquine for COVID-19 Treatment via Social Media
This paper studies the reactions of social network users on the recommendation of using HCQ for COVID-19 treatment by analyzing the reaction patterns and sentiment of the tweets.
Improved sparse PCA method for face and image recognition
Experimental results illustrate that the accuracy of the combination of the sparse PCA method (using the proximal gradient method and the FISTA method) and one specific classification system may be lower than the accuracy of the combination of the PCA method and one specific classification system but sometimes the combination of the sparse PCA method (using the proximal gradient method or the FISTA method) and one specific classification system leads to better accuracy.
Text classification problems via BERT embedding method and graph convolutional neural network
This paper presents the novel way combining the BERT embedding method and the graph convolutional neural network.
Droplet ejection by electrowetting actuation
In this study, we show that the size of the satellite droplet and the ejection time are not only dependent on the contact-line velocity, which directly relates to the applied voltage enabling the electrowetting effect, but also affected by the ejection dynamics.
Fluid Dynamics
Predicting Sample Collision with Neural Networks
Many state-of-art robotics applications require fast and efficient motion planning algorithms.
Solve fraud detection problem by using graph based learning methods
In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem.
Point cloud denoising based on tensor Tucker decomposition
In this paper, we propose a new algorithm for point cloud denoising based on the tensor Tucker decomposition.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications.
Hedera: Scalable Indexing and Exploring Entities in Wikipedia Revision History
Much of work in semantic web relying on Wikipedia as the main source of knowledge often work on static snapshots of the dataset.
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events
In this work, we present a novel approach for timeline summarization of high-impact events, which uses entities instead of sentences for summarizing the event at each individual point in time.
