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 671–680 of 4891 papers

TitleStatusHype
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual TasksCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive HypergraphsCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion ModelsCode1
KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series ForecastingCode1
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and MappingCode1
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified