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Project or Job Scheduling

Papers

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Showing 2526–2550 of 3104 papers

TitleStatusHype
Caramel: Accelerating Decentralized Distributed Deep Learning with Computation Scheduling—0
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
Accurate Graph Filtering in Wireless Sensor Networks—0
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented DialogCode1
Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing SystemCode3
Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping—0
A Novel Multi-Agent System for Complex Scheduling Problems—0
Heterogeneous CPU+GPU Stochastic Gradient Descent AlgorithmsCode0
Single-step Adversarial training with Dropout Scheduling—0
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems—0
Quantum Approximation for Multi-Scale SchedulingCode0
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling—0
Long-term scheduling and power control for wirelessly powered cell-free IoT—0
Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service—0
Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit FleetsCode0
Robust Dual Control based on Gain Scheduling—0
Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output—0
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness—0
Scheduling Flexible Non-Preemptive Loads in Smart-Grid Networks—0
Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints—0
A hybrid optimization procedure for solving a tire curing scheduling problem—0
Moving horizon-based optimal scheduling of EV charging: A power system-cognizant approach—0
Multi-User Remote lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm—0
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling—0
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
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