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 2921–2930 of 4891 papers

TitleStatusHype
Optimizing Distributed Training on Frontier for Large Language Models—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
Aggregating Multiple Bio-Inspired Image Region Classifiers For Effective And Lightweight Visual Place Recognition—0
Modeling non-linear Effects with Neural Networks in Relational Event ModelsCode0
YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI—0
EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping—0
Agent Assessment of Others Through the Lens of Self—0
Linear Attention via Orthogonal Memory—0
A Multimodal Approach for Advanced Pest Detection and Classification—0
Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified