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 4451–4475 of 4891 papers

TitleStatusHype
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction—0
Training Structured Efficient Convolutional Layers—0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference AggregationCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Combinatorial Attacks on Binarized Neural Networks—0
Rough set based lattice structure for knowledge representation in medical expert systems: low back pain management case study—0
Fairness-aware Classification: Criterion, Convexity, and Bounds—0
Bayesian Structure Learning by Recursive Bootstrap—0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Bayesian Patchworks: An Approach to Case-Based Reasoning—0
Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline ExtractionCode0
RetGK: Graph Kernels based on Return Probabilities of Random Walks—0
A Deeper Look at 3D Shape Classifiers—0
Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency—0
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN—0
Training Binary Weight Networks via Semi-Binary Decomposition—0
Attention Gated Networks: Learning to Leverage Salient Regions in Medical ImagesCode0
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks—0
Fast and Robust Matching for Multimodal Remote Sensing Image Registration—0
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical ModelsCode0
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement—0
Rethinking Numerical Representations for Deep Neural Networks—0
The Sample Complexity of Up-to- Multi-Dimensional Revenue Maximization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified