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 2631–2640 of 4891 papers

TitleStatusHype
RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing—0
A lightweight target detection algorithm based on Mobilenet Convolution—0
RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model—0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
RWKV-Lite: Deeply Compressed RWKV for Resource-Constrained Devices—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction—0
S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation—0
S7: Selective and Simplified State Space Layers for Sequence Modeling—0
SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified