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

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Showing 2101–2110 of 4891 papers

TitleStatusHype
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
Episodic Memory for Learning Subjective-Timescale Models—0
CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection—0
An Extended Integral Unit Commitment Formulation and an Iterative Algorithm for Convex Hull Pricing—0
A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data—0
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction—0
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference—0
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection—0
Ensemble Learning Based Convex Approximation of Three-Phase Power Flow—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified