18 citations · 29 across the 9 of their papers we have counts for
9 papers
ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching
Jong Woo Nam, Amanda S. Rios, Bartlett W. Mel
Object recognition (OR) in humans relies heavily on shape cues and the ability to recognize objects across varying 3D viewpoints. Unlike humans, deep networks often rely on non-sha…
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…
ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching
Jong Woo Nam, Amanda S. Rios, Bartlett W. Mel
Object recognition in humans depends primarily on shape cues. We have developed a new approach to measuring the shape recognition performance of a vision system based on nearest ne…
Lifelong Learning Without a Task Oracle
Amanda Rios, Laurent Itti
Supervised deep neural networks are known to undergo a sharp decline in the accuracy of older tasks when new tasks are learned, termed "catastrophic forgetting". Many state-of-the-…
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…
Closed-Loop Memory GAN for Continual Learning
Amanda Rios, Laurent Itti
Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic for…