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 801–810 of 4891 papers

TitleStatusHype
Combining distribution-based neural networks to predict weather forecast probabilitiesCode0
Improving Hyper-Relational Knowledge Graph CompletionCode0
Improving Variational Auto-Encoders using Householder FlowCode0
Arbitrary-Oriented Scene Text Detection via Rotation ProposalsCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret PerformanceCode0
Improving Generalization of Medical Image Registration Foundation ModelCode0
Implicit Regularization for Optimal Sparse RecoveryCode0
A Fast Bootstrap Algorithm for Causal Inference with Large DataCode0
Implicit Generative Prior for Bayesian Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified