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 3771–3780 of 4891 papers

TitleStatusHype
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay—0
Low Resource Quadratic Forms for Knowledge Graph Embeddings—0
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
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
Show:102550
← PrevPage 378 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified