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 2981–2990 of 4891 papers

TitleStatusHype
Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
A Model Stealing Attack Against Multi-Exit Networks—0
Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation—0
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel InferenceCode0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
RWKV: Reinventing RNNs for the Transformer EraCode6
GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified