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 1021–1030 of 4891 papers

TitleStatusHype
Continual Learning via Online Leverage Score Sampling—0
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation—0
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs—0
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
Anomaly Detection with Tensor Networks—0
CNN Mixture-of-Depths—0
Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications—0
Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified