33 citations · 40 across the 3 of their papers we have counts for
8 papers
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…