SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4441–4450 of 4891 papers

TitleStatusHype
evS2CP: Real-time Simultaneous Speed and Charging Planner for Connected Electric Vehicles—0
Exact Acceleration of Subgraph Graph Neural Networks by Eliminating Computation Redundancy—0
Exact Characterization of Aggregate Flexibility via Generalized Polymatroids—0
Exact first passage time distribution for second-order reactions in chemical networks—0
Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making—0
Exp-Concavity of Proper Composite Losses—0
Expected Sliced Transport Plans—0
ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference—0
Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection—0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified