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 451–460 of 4891 papers

TitleStatusHype
Efficient algorithms for the Hadamard decompositionCode0
FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV TrackingCode1
Integrating Locality-Aware Attention with Transformers for General Geometry PDEs—0
Non-Uniform Class-Wise Coreset Selection: Characterizing Category Difficulty for Data-Efficient Transfer Learning—0
Mask Image WatermarkingCode1
RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity—0
Hadamard product in deep learning: Introduction, Advances and Challenges—0
Spike-Kal: A Spiking Neuron Network Assisted Kalman Filter—0
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical featuresCode0
BitNet b1.58 2B4T Technical Report—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified