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 3776–3800 of 4891 papers

TitleStatusHype
Computational Efficiency in Multivariate Adversarial Risk Analysis Models—0
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox DetectorCode0
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties—0
Scalable Smartphone Cluster for Deep Learning—0
Stochastic Primal-Dual Deep Unrolling—0
Counting Objects by Diffused Index: geometry-free and training-free approach—0
On Learning the Transformer KernelCode0
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies—0
Action-Sufficient State Representation Learning for Control with Structural Constraints—0
RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss—0
Streaming on-device detection of device directed speech from voice and touch-based invocation—0
A study on the efficacy of model pre-training in developing neural text-to-speech system—0
Predictive Maintenance for General Aviation Using Convolutional Transformers—0
Fast and Interpretable Consensus Clustering via Minipatch Learning—0
Graph Coloring: Comparing Cluster Graphs to Factor Graphs—0
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression—0
A new weakly supervised approach for ALS point cloud semantic segmentation—0
Learn to Communicate with Neural Calibration: Scalability and Generalization—0
Understanding the Variance Collapse of SVGD in High Dimensions—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Equivariant Transformers for Neural Network based Molecular Potentials—0
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION—0
MAGNEx: A Model Agnostic Global Neural Explainer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified