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 4576–4600 of 4891 papers

TitleStatusHype
Image Super-Resolution Using Dense Skip ConnectionsCode0
A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data—0
Inference in Graphical Models via Semidefinite Programming Hierarchies—0
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving—0
DPC-Net: Deep Pose Correction for Visual LocalizationCode0
Locating 3D Object Proposals: A Depth-Based Online Approach—0
Roll-back Hamiltonian Monte Carlo—0
Supervised Dimensionality Reduction for Big DataCode0
Dynamic Tensor Clustering—0
Deep vs. Diverse Architectures for Classification Problems—0
Portfolio Optimization with Entropic Value-at-Risk—0
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism—0
Learning Deep Architectures via Generalized Whitened Neural Networks—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
Attentional Correlation Filter Network for Adaptive Visual TrackingCode0
featsel: A framework for benchmarking of feature selection algorithms and cost functionsCode1
Fast Screening Algorithm for Rotation and Scale Invariant Template Matching—0
Learning linear structural equation models in polynomial time and sample complexity—0
Discriminative Optimization: Theory and Applications to Computer Vision Problems—0
Tensor-Based Backpropagation in Neural Networks with Non-Sequential Input—0
Bayesian Models of Data Streams with Hierarchical Power Priors—0
Multilingual Hierarchical Attention Networks for Document ClassificationCode0
Noise-Blind Image Deblurring—0
Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior—0
Dynamic Time-Of-Flight—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified