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 3251–3260 of 4891 papers

TitleStatusHype
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-ConcatenationCode0
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness—0
Leveraging object detection for the identification of lung cancer—0
Union Subgraph Neural NetworksCode0
Bi-fidelity Variational Auto-encoder for Uncertainty QuantificationCode0
Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction—0
Deep Reinforcement Learning with Plasticity Injection—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
A Model Stealing Attack Against Multi-Exit Networks—0
Show:102550
← PrevPage 326 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified