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 3621–3630 of 4891 papers

TitleStatusHype
Two-Step Question Retrieval for Open-Domain QACode0
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance RankingCode0
Perfect Spectral Clustering with Discrete CovariatesCode0
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems—0
Scalable Vehicle Re-Identification via Self-Supervision—0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling—0
Contingency-constrained economic dispatch with safe reinforcement learning—0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified