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 691–700 of 4891 papers

TitleStatusHype
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive HypergraphsCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP StandardsCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
LIMOPro: Reasoning Refinement for Efficient and Effective Test-time ScalingCode1
Linear Reduced Order Model Predictive ControlCode1
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual TasksCode1
Deep Learning for Hate Speech Detection: A Comparative StudyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified