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 681–690 of 4891 papers

TitleStatusHype
Language Model Uncertainty Quantification with Attention ChainCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual TasksCode1
LDM-Morph: Latent diffusion model guided deformable image registrationCode1
Deep Implicit Moving Least-Squares Functions for 3D ReconstructionCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified