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 1691–1700 of 4891 papers

TitleStatusHype
Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences—0
Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability—0
TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality ModelCode1
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs—0
AIM 2024 Challenge on UHD Blind Photo Quality AssessmentCode1
Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint—0
Zero-Shot Detection of AI-Generated Images—0
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of ExpertsCode4
Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification—0
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified