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 2431–2440 of 4891 papers

TitleStatusHype
Contextual Multinomial Logit Bandits with General Value Functions—0
Anchor-based Large Language ModelsCode1
On Computationally Efficient Multi-Class Calibration—0
Mercury: A Code Efficiency Benchmark for Code Large Language ModelsCode2
Accelerating Distributed Deep Learning using Lossless Homomorphic CompressionCode0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
Differentially Private Training of Mixture of Experts Models—0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data StreamsCode0
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive LossCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified