Search Results for author:
Found 16 papers, 3 papers with code
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to a target domain using only unlabeled target data.
Ranked #1 on
Domain Adaptation
on VisDA2017
Localizing Moments of Actions in Untrimmed Videos of Infants with Autism Spectrum Disorder
This study is the first to conduct end-to-end temporal action localization in untrimmed videos of infants with ASD, offering promising avenues for early intervention and support.
MobilityGPT: Enhanced Human Mobility Modeling with a GPT model
Then, we constrained the training process via a road connectivity matrix that provides the connectivity of sequences in trajectory generation, thereby keeping generated trajectories in geospatial limits.
VeCLIP: Improving CLIP Training via Visual-enriched Captions
For example, VeCLIP achieves up to +25. 2% gain in COCO and Flickr30k retrieval tasks under the 12M setting.
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
High-dimensional data are widely used in the era of deep learning with numerous applications.
Benchmarking Adversarial Robustness of Compressed Deep Learning Models
Our findings reveal that while the benefits of pruning enhanced generalizability, compression, and faster inference times are preserved, adversarial robustness remains comparable to the base model.
Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach
The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction.
PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation
We then utilize CLIP's zero-shot prediction to formulate a Pseudo-labeling setting with Adaptive Debiasing in CLIP (PADCLIP) by adjusting causal inference with our momentum and CFM.
Ranked #3 on
Unsupervised Domain Adaptation
on DomainNet
Differentially Private Generative Adversarial Networks with Model Inversion
To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in which controlled noise is added to the gradients.
A Natural Language Processing and Deep Learning based Model for Automated Vehicle Diagnostics using Free-Text Customer Service Reports
In recent years, numerous studies have investigated data-driven approaches to improve the vehicle diagnostics process using available vehicle data.
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?
The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46%.
Clinical Validation of Single-Chamber Model-Based Algorithms Used to Estimate Respiratory Compliance
Non-invasive estimation of respiratory physiology using computational algorithms promises to be a valuable technique for future clinicians to detect detrimental changes in patient pathophysiology.
Minimizing L1 over L2 norms on the gradient
Consequently, we postulate that applying L1/L2 on the gradient is better than the classic total variation (the L1 norm on the gradient) to enforce the sparsity of the image gradient.
Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode
Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
We consider the problem of detecting a few targets among a large number of hierarchical data streams.
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest.
