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 4176–4200 of 4891 papers

TitleStatusHype
The 4th AI City Challenge—0
Automatic exposure selection and fusion for high-dynamic-range photography via smartphones—0
Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective—0
Biophysically detailed mathematical models of multiscale cardiac active mechanicsCode0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
Encoder blind combinatorial compressed sensing—0
Cortical surface registration using unsupervised learningCode0
Towards Reusable Network Components by Learning Compatible Representations—0
Direct loss minimization algorithms for sparse Gaussian processesCode0
Radon cumulative distribution transform subspace modeling for image classificationCode0
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking—0
Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model—0
Coping With Simulators That Don't Always ReturnCode0
Multi-target regression via output space quantization—0
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems—0
A High-Performance Object Proposals based on Horizontal High Frequency Signal—0
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching—0
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles—0
Energy-efficient and Robust Cumulative Training with Net2Net Transformation—0
Learning Directly from Grammar Compressed TextCode0
A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation—0
Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models—0
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified