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 4376–4400 of 4891 papers

TitleStatusHype
Preference Neural NetworkCode0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing ImagesCode1
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action RecognitionCode0
Person Identification with Visual Summary for a Safe Access to a Smart Home—0
Spherical U-Net on Cortical Surfaces: Methods and Applications—0
Understanding Neural Architecture Search Techniques—0
An LBP-HOG Descriptor Based on Matrix Projection For Mammogram Classification—0
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis—0
How Can We Be So Dense? The Benefits of Using Highly Sparse RepresentationsCode1
Topology-based Representative Datasets to Reduce Neural Network Training ResourcesCode0
Efficient Search-Based Weighted Model Integration—0
PointNetLK: Robust & Efficient Point Cloud Registration using PointNetCode0
Low-rank Tensor Grid for Image Completion—0
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
Composite Event Recognition for Maritime Monitoring—0
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC—0
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite ProgrammingCode0
GCNv2: Efficient Correspondence Prediction for Real-Time SLAMCode0
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning—0
Nonlinear Approximation via Compositions—0
SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection—0
Appearance-based Gesture recognition in the compressed domain—0
Significance Tests for Neural NetworksCode0
Fast Task-Aware Architecture Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified