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 4151–4175 of 4891 papers

TitleStatusHype
The deterministic information bottleneckCode0
Rewarded meta-pruning: Meta Learning with Rewards for Channel PruningCode0
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under UncertaintyCode0
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference AggregationCode0
RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex ScenariosCode0
I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in Social RobotsCode0
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster ImagesCode0
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical SystemsCode0
SpecNet2: Orthogonalization-free spectral embedding by neural networksCode0
RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image RegistrationCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
On the Completeness of Invariant Geometric Deep Learning ModelsCode0
Jointly Efficient and Optimal Algorithms for Logistic BanditsCode0
DBgDel: Database-Enhanced Gene Deletion Framework for Growth-Coupled Production in Genome-Scale Metabolic ModelsCode0
On the Computational Efficiency of Training Neural NetworksCode0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline ExtractionCode0
A Social Robot with Inner Speech for Dietary GuidanceCode0
On the Effectiveness of Incremental Training of Large Language ModelsCode0
Riemannian Laplace Approximation with the Fisher MetricCode0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
On the Estimation of Entropy in the FastICA AlgorithmCode0
K^2IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson ProcessesCode0
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and DeploymentCode0
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional ElementsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified