Search Results for author:
Found 12 papers, 1 papers with code
Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving
To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems.
VIAssist: Adapting Multi-modal Large Language Models for Users with Visual Impairments
However, it is challenging for VI people to use MLLMs due to the difficulties in capturing the desirable images to fulfill their daily requests.
EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
We implement EdgeFM using two FMs on two edge platforms.
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease
ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner.
Indoor Smartphone SLAM with Learned Echoic Location Features
Indoor self-localization is a highly demanded system function for smartphones.
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge
This paper presents extensive evaluation of Sardino's performance in counteracting adversarial examples and applies it to build a real-time car-borne traffic sign recognition system.
Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization
Achieving efficient execution of machine learning models has attracted significant attention recently.
Regret Analysis of Bandit Problems with Causal Background Knowledge
For example, we observe that even with a few hundreds of iterations, the regret of causal algorithms is less than that of standard algorithms by a factor of three.
Moving Target Defense for Deep Visual Sensing against Adversarial Examples
Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results.
A Blended Deep Learning Approach for Predicting User Intended Actions
In this work, we focus on pre- dicting attrition, which is one of typical user intended actions.
Deep Neural Net with Attention for Multi-channel Multi-touch Attribution
Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka.
Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising
Predicting keywords performance, such as number of impressions, click-through rate (CTR), conversion rate (CVR), revenue per click (RPC), and cost per click (CPC), is critical for sponsored search in the online advertising industry.
