18 citations · 40 across the 8 of their papers we have counts for
8 papers
Lightweight Learner for Shared Knowledge Lifelong Learning
Yunhao Ge, Yuecheng Li, Di Wu +12
In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicat…
Ferroelectric FET based Context-Switching FPGA Enabling Dynamic Reconfiguration for Adaptive Deep Learning Machines
Yixin Xu, Zijian Zhao, Yi Xiao +16
Field Programmable Gate Array (FPGA) is widely used in acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, c…
What can we learn from misclassified ImageNet images?
Shixian Wen, Amanda Sofie Rios, Kiran Lekkala +1
Understanding the patterns of misclassified ImageNet images is particularly important, as it could guide us to design deep neural networks (DNN) that generalize better. However, th…
Beneficial Perturbation Network for designing general adaptive artificial intelligence systems
Shixian Wen, Amanda Rios, Yunhao Ge +1
The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural network…
Beneficial Perturbations Network for Defending Adversarial Examples
Shixian Wen, Amanda Rios, Laurent Itti
Deep neural networks can be fooled by adversarial attacks: adding carefully computed small adversarial perturbations to clean inputs can cause misclassification on state-of-the-art…
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system
Shixian Wen, Laurent Itti
Adversarial training, in which a network is trained on both adversarial and clean examples, is one of the most trusted defense methods against adversarial attacks. However, there a…
Beneficial perturbation network for continual learning
Shixian Wen, Laurent Itti
Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during l…
Overcoming catastrophic forgetting problem by weight consolidation and long-term memory
Shixian Wen, Laurent Itti
Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during l…