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Found 8 papers, 1 papers with code
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT
Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets.
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning
Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warnings through the deployment of a network of IoT sensors.
Intelligent Communication
Multi-agent Reinforcement Learning
+1
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time.
Autonomous Curiosity for Real-Time Training Onboard Robotic Agents
Learning requires both study and curiosity.
TickTalk -- Timing API for Dynamically Federated Cyber-Physical Systems
Although timing and synchronization of a dynamically-changing set of elements and their related power considerations are essential to many cyber-physical systems (CPS), they are absent from today's programming languages, forcing programmers to handle these matters outside of the language and on a case-by-case basis.
Programming Languages Systems and Control
Learning to Learn in Simulation
We use a 3D simulation environment and deep reinforcement learning to train a curiosity agent to, in turn, train the object detection model.
ClickBAIT-v2: Training an Object Detector in Real-Time
Modern deep convolutional neural networks (CNNs) for image classification and object detection are often trained offline on large static datasets.
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks
Today's general-purpose deep convolutional neural networks (CNN) for image classification and object detection are trained offline on large static datasets.
