104 citations · 283 across the 11 of their papers we have counts for
13 papers
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics
Onat Gungor, Tajana Rosing, Baris Aksanli
Industrial Internet of Things (I-IoT) is a collaboration of devices, sensors, and networking equipment to monitor and collect data from industrial operations. Machine learning (ML)…
MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory
Saransh Gupta, Rosario Cammarota, Tajana Rosing
The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in health…
RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing
Onat Gungor, Tajana Rosing, Baris Aksanli
Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring devices and analyzing collected data. Machine learning methods are commo…
A Theoretical Perspective on Hyperdimensional Computing
Anthony Thomas, Sanjoy Dasgupta, Tajana Rosing
Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining high-dimensional, low-precision, distributed representations of data. These representations can…
SHEARer: Highly-Efficient Hyperdimensional Computing by Software-Hardware Enabled Multifold Approximation
Behnam Khaleghi, Sahand Salamat, Anthony Thomas +3
Hyperdimensional computing (HD) is an emerging paradigm for machine learning based on the evidence that the brain computes on high-dimensional, distributed, representations of data…
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Yunhui Guo, Yandong Li, Liqiang Wang +1
There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which a…
A Broader Study of Cross-Domain Few-Shot Learning
Yunhui Guo, Noel C. Codella, Leonid Karlinsky +5
Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applicatio…
Improved Schemes for Episodic Memory-based Lifelong Learning
Yunhui Guo, Mingrui Liu, Tianbao Yang +1
Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to g…
Workload-Aware Opportunistic Energy Efficiency in Multi-FPGA Platforms
Sahand Salamat, Behnam Khaleghi, Mohsen Imani +1
The continuous growth of big data applications with high computational and scalability demands has resulted in increasing popularity of cloud computing. Optimizing the performance…
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
Yunhui Guo, Yandong Li, Rogerio Feris +2
There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can…
SpotTune: Transfer Learning through Adaptive Fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar +3
Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning wi…
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework
Mohsen Imani, Mohammad Samragh, Yeseong Kim +3
Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-the-art D…
Power Saving Techniques for Wireless LANs
T. Simunic
Fast wireless access has rapidly become commonplace. Wireless access points and Hotspot servers are sprouting everywhere. Battery lifetime continues to be a critical issue in mobil…