7 citations · 17 across the 6 of their papers we have counts for
6 papers
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning
Yi Hu, Jinhang Zuo, Bob Iannucci +1
Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warn…
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Yi Hu, Chaoran Zhang, Edward Andert +6
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-…
Autonomous Curiosity for Real-Time Training Onboard Robotic Agents
Ervin Teng, Bob Iannucci
Learning requires both study and curiosity. A good learner is not only good at extracting information from the data given to it, but also skilled at finding the right new informati…
Learning to Learn in Simulation
Ervin Teng, Bob Iannucci
Deep learning often requires the manual collection and annotation of a training set. On robotic platforms, can we partially automate this task by training the robot to be curious,…
ClickBAIT-v2: Training an Object Detector in Real-Time
Ervin Teng, Rui Huang, Bob Iannucci
Modern deep convolutional neural networks (CNNs) for image classification and object detection are often trained offline on large static datasets. Some applications, however, will…
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks
Ervin Teng, João Diogo Falcão, Bob Iannucci
Today's general-purpose deep convolutional neural networks (CNN) for image classification and object detection are trained offline on large static datasets. Some applications, howe…