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 2441–2450 of 4891 papers

TitleStatusHype
AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers—0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular FusionCode2
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
BEBLID: Boosted efficient binary local image descriptorCode2
On the Completeness of Invariant Geometric Deep Learning ModelsCode0
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image SegmentationCode4
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Partially Stochastic Infinitely Deep Bayesian Neural NetworksCode0
Show:102550
← PrevPage 245 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified