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
A few-shot Label Unlearning in Vertical Federated Learning—0
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds—0
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing—0
Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control—0
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT—0
A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates—0
A Rank-SVM Approach to Anomaly Detection—0
A 3D grain-based reconstruction method from a 2D surface image for the Distinct Lattice Spring Model—0
Radiance Surfaces: Optimizing Surface Representations with a 5D Radiance Field Loss—0
A Question of Time: Revisiting the Use of Recursive Filtering for Temporal Calibration of Multisensor Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified