Search Results for author:
Found 23 papers, 16 papers with code
Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions
This paper presents an approach that enables a robot to rapidly learn the complete 3D model of a given object for manipulation in unfamiliar orientations.
PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators
PhyPlan leverages PINNs to simulate and predict outcomes of actions in a fast and accurate manner and uses MCTS for planning.
CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments
CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores.
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model
The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm.
DRAGON: Decentralized Fault Tolerance in Edge Federations
Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers.
RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting
We consider the specific use case of road traffic systems where incidents take the form of anomalous events, such as accidents or broken-down vehicles.
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
We also propose a novel NAS policy, called BOSHNAS, that leverages this new scheme, Bayesian modeling, and second-order optimization, to quickly train and use a neural surrogate model to converge to the optimal architecture.
MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments.
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments
This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mobile edge computing platforms, specifically in mission-critical domains like surveillance and healthcare.
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction Following
Our goal is to enable a robot to learn how to sequence its actions to perform tasks specified as natural language instructions, given successful demonstrations from a human partner.
CAROL: Confidence-Aware Resilience Model for Edge Federations
To address this, we present a confidence aware resilience model, CAROL, that utilizes a memory-efficient generative neural network to predict the Quality of Service (QoS) for a future state and a confidence score for each prediction.
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications.
GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments
Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached.
MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements.
PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing
Building a fault-tolerant edge system that can quickly react to node overloads or failures is challenging due to the unreliability of edge devices and the strict service deadlines of modern applications.
HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud.
Generative Optimization Networks for Memory Efficient Data Generation
In standard generative deep learning models, such as autoencoders or GANs, the size of the parameter set is proportional to the complexity of the generated data distribution.
TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators
We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot.
Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks
The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources.
ToolNet: Using Commonsense Generalization for Predicting Tool Use for Robot Plan Synthesis
When compared to a graph neural network baseline, it achieves 14-27% accuracy improvement for predicting known tools from new world scenes, and 44-67% improvement in generalization for novel objects not encountered during training.
Robotics
AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices
Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia.
APEX: Adaptive Ext4 File System for Enhanced Data Recoverability in Edge Devices
We demonstrate the effectiveness of APEX through a case study of overwriting surveillance videos by CryPy malware on Raspberry-Pi based Edge deployment and show 678% and 32% higher recovery than Ext4 and current state-of-the-art File Systems.
Operating Systems
FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing
The requirement of supporting both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity for integrating Edge, Fog and Cloud infrastructure.
Distributed, Parallel, and Cluster Computing
