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 4361–4370 of 4891 papers

TitleStatusHype
Two-stage Best-scored Random Forest for Large-scale Regression—0
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks—0
PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis—0
DS-VIO: Robust and Efficient Stereo Visual Inertial Odometry based on Dual Stage EKF—0
Accelerated Sparse Recovery Under Structured Measurements—0
Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine—0
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature PointsCode0
Language Models with TransformersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified