163 citations · 206 across the 11 of their papers we have counts for
12 papers
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
Weiming Zhuang, Jiabo Huang, Jingtao Li +4
Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between t…
Empirical Recipes for Efficient and Compact Vision-Language Models
Jiabo Huang, Zhizhong Li, Sina Sajadmanesh +2
Deploying vision-language models (VLMs) in resource-constrained settings demands low latency and high throughput, yet existing compact VLMs often fall short of the inference speedu…
StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold
Zhizhong Li, Sina Sajadmanesh, Jingtao Li +1
Low-rank adaptation (LoRA) has been widely adopted as a parameter-efficient technique for fine-tuning large-scale pre-trained models. However, it still lags behind full fine-tuning…
Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy
David Schneider, Sina Sajadmanesh, Vikash Sehwag +4
Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence. Prevalent methods tackling this problem use differential privac…
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees
Sina Sajadmanesh, Daniel Gatica-Perez
Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive inf…
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet +1
In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Pert…
Locally Private Graph Neural Networks
Sina Sajadmanesh, Daniel Gatica-Perez
Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise…
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks
Sina Sajadmanesh, Sogol Bazargani, Jiawei Zhang +1
Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are…
NPGLM: A Non-Parametric Method for Temporal Link Prediction
Sina Sajadmanesh, Jiawei Zhang, Hamid R. Rabiee
In this paper, we try to solve the problem of temporal link prediction in information networks. This implies predicting the time it takes for a link to appear in the future, given…
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh +5
Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning mo…
Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web
Sina Sajadmanesh, Sina Jafarzadeh, Seyed Ali Osia +6
Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around…
Predicting Anchor Links between Heterogeneous Social Networks
Sina Sajadmanesh, Hamid R. Rabiee, Ali Khodadadi
People usually get involved in multiple social networks to enjoy new services or to fulfill their needs. Many new social networks try to attract users of other existing networks to…