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 3521–3530 of 4891 papers

TitleStatusHype
Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition—0
Efficient Kernel UCB for Contextual BanditsCode0
Scale-free Unconstrained Online Learning for Curved Losses—0
Real-time decision-making for autonomous vehicles under faults—0
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural NetworksCode0
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture SearchCode0
DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions—0
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials—0
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label FusionCode1
On the Complexity of Winner Determination and Strategic Control in Conditional Approval Voting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified