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 871–880 of 4891 papers

TitleStatusHype
Activation-wise Propagation: A Universal Strategy to Break Timestep Constraints in Spiking Neural Networks for 3D Data Processing—0
HeadInfer: Memory-Efficient LLM Inference by Head-wise OffloadingCode2
Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models—0
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints—0
Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control—0
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization—0
iMOVE: Instance-Motion-Aware Video Understanding—0
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRACode0
IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting—0
Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table ReasoningCode1
Show:102550
← PrevPage 88 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified