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 1011–1020 of 4891 papers

TitleStatusHype
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated LearningCode0
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum OptimizationCode0
Arbitrary-Oriented Scene Text Detection via Rotation ProposalsCode0
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity RecognitionCode0
Group and Shuffle: Efficient Structured Orthogonal ParametrizationCode0
Graph Neural Networks for modelling breast biomechanical compressionCode0
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based UpsamplingCode0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
Graph neural networks informed locally by thermodynamicsCode0
Graph Learning from Data under Structural and Laplacian ConstraintsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified