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 3431–3440 of 4891 papers

TitleStatusHype
Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics—0
Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability EstimationCode0
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable—0
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing—0
Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class ClassificationCode1
Approximate Message Passing with Parameter Estimation for Heavily Quantized MeasurementsCode0
How Useful are Gradients for OOD Detection Really?—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance RankingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified