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 4751–4775 of 4891 papers

TitleStatusHype
SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus—0
Towards Good Practices for Very Deep Two-Stream ConvNetsCode1
Ranking Preserving Hashing for Fast Similarity Search—0
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random BasesCode0
The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes—0
Sparse Projection Oblique Randomer ForestsCode1
Hierarchical recurrent neural network for skeleton based action recognition—0
Pairwise Geometric Matching for Large-Scale Object Retrieval—0
The k-Support Norm and Convex Envelopes of Cardinality and Rank—0
Hardware Compliant Approximate Image Codes—0
Illumination and Reflectance Spectra Separation of a Hyperspectral Image Meets Low-Rank Matrix Factorization—0
MatchNet: Unifying Feature and Metric Learning for Patch-Based MatchingCode0
Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-Level Appearance Models—0
Fast and Accurate Image Upscaling With Super-Resolution Forests—0
Discriminative Shape From Shading in Uncalibrated Illumination—0
Hierarchical structure-and-motion recovery from uncalibrated images—0
A Practical Guide to Randomized Matrix Computations with MATLAB ImplementationsCode0
Inner and Inter Label Propagation: Salient Object Detection in the WildCode0
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems—0
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling—0
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data—0
Classification of Occluded Objects using Fast Recurrent Processing—0
Template Kernels for Dependency ParsingCode0
Sampling Techniques for Streaming Cross Document Coreference Resolution—0
When and why are log-linear models self-normalizing?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified