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 861–870 of 4891 papers

TitleStatusHype
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping—0
Improving Prostate Gland Segmenting Using Transformer based Architectures—0
Hybrid Polynomial Zonotopes: A Set Representation for Reachability Analysis in Hybrid Nonaffine Systems—0
Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection—0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localizations for better covariance estimation—0
IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model—0
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified