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 1961–1970 of 4891 papers

TitleStatusHype
Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks—0
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image ClassificationCode1
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinesCode0
Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control—0
Machine Unlearning for Medical Imaging—0
Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation—0
A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network—0
PCAC-GAN: A Sparse-Tensor-Based Generative Adversarial Network for 3D Point Cloud Attribute Compression—0
Learning local equivariant representations for quantum operatorsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified