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 2471–2480 of 4891 papers

TitleStatusHype
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial AttacksCode2
Open-RadVLAD: Fast and Robust Radar Place RecognitionCode1
Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones—0
Linear Periodically Time-Variant Digital PLL Phase Noise Modeling Using Conversion Matrices and Uncorrelated UpsamplingCode0
Spectral Clustering for Discrete Distributions—0
Learning to Manipulate Artistic ImagesCode1
MambaByte: Token-free Selective State Space Model—0
Unified-Width Adaptive Dynamic Network for All-In-One Image RestorationCode1
Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction—0
RefreshNet: Learning Multiscale Dynamics through Hierarchical Refreshing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified