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 2381–2390 of 4891 papers

TitleStatusHype
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition—0
Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine TransformationsCode1
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling—0
SparseLLM: Towards Global Pruning for Pre-trained Language ModelsCode2
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging—0
Quasi-Bayesian Estimation and Inference with Control Functions—0
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations—0
ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection For Feature ReductionCode0
Fast Algorithms for Quantile Regression with Selection—0
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified