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 3201–3225 of 4891 papers

TitleStatusHype
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks—0
STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow PredictionCode0
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance ScalingCode1
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-LabelsCode1
Learning to Optimize in Model Predictive Control—0
Regularized ERM on random subspaces—0
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping—0
Robust and Fast Measure of Information via Low-rank RepresentationCode0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot SystemsCode1
Towards More Robust Interpretation via Local Gradient AlignmentCode0
Machine Learning Accelerated PDE Backstepping Observers—0
Fast-SNARF: A Fast Deformer for Articulated Neural FieldsCode2
Condensed Gradient BoostingCode0
Mutual Guidance and Residual Integration for Image Enhancement—0
Spatial-Spectral Transformer for Hyperspectral Image DenoisingCode1
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification—0
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural NetworksCode1
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst DesignCode0
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning—0
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network—0
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision TransformersCode0
Enhancing Mobile Robot Navigation Safety and Efficiency through NMPC with Relaxed CBF in Dynamic Environments—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified