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 3021–3030 of 4891 papers

TitleStatusHype
A 3D grain-based reconstruction method from a 2D surface image for the Distinct Lattice Spring Model—0
A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators—0
Near-Field MIMO-ISAR Millimeter-Wave Imaging—0
A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code RepresentationCode1
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object DetectionCode1
Posterior Sampling for Deep Reinforcement LearningCode1
Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise RankingCode0
Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis—0
Learning battery model parameter dynamics from data with recursive Gaussian process regression—0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified