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Showing 2401–2425 of 3104 papers

TitleStatusHype
Towards Continually Learning Application Performance Models—0
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach—0
Toward Secure and Private Over-the-Air Federated Learning—0
Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans—0
Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels—0
ModServe: Scalable and Resource-Efficient Large Multimodal Model Serving—0
Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity—0
Towards Energy and Time Efficient Resource Allocation in IoT-Fog-Cloud Environment—0
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods—0
Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning—0
Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling—0
Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges—0
Towards KAB2S: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem—0
Toward Smart Scheduling in Tapis—0
Towards model predictive control of supercritical CO2 cycles—0
Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction—0
Towards Optimal System Scheduling with Synthetic Inertia Provision from Wind Turbines—0
Towards Practical Deep Schedulers for Allocating Cellular Radio Resources—0
Towards Resource-Efficient Compound AI Systems—0
Towards Robust and Scalable Dispatch Modeling of Long-Duration Energy Storage—0
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning—0
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks—0
Towards Sustainable Satellite Edge Computing—0
Towards Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
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