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 371–380 of 4891 papers

TitleStatusHype
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation—0
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment—0
Dendritic Computing with Multi-Gate Ferroelectric Field-Effect Transistors—0
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
Gaussian Process Policy Iteration with Additive Schwarz Acceleration for Forward and Inverse HJB and Mean Field Game Problems—0
Dimension-reduced Optimization of Multi-zone Thermostatically Controlled LoadsCode0
Multi-start Optimization Method via Scalarization based on Target Point-based Tchebycheff Distance for Multi-objective Optimization—0
Do global forecasting models require frequent retraining?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified