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 2051–2060 of 4891 papers

TitleStatusHype
YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series—0
Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing DetectionCode2
Analyzing Large Language Models for Classroom Discussion AssessmentCode0
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization—0
RILe: Reinforced Imitation Learning—0
PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image EnhancementCode1
StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified