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 971–980 of 4891 papers

TitleStatusHype
Latent Lexical Projection in Large Language Models: A Novel Approach to Implicit Representation Refinement—0
Comply: Learning Sentences with Complex Weights inspired by Fruit Fly OlfactionCode0
DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users—0
End-to-End Imitation Learning for Optimal Asteroid Proximity Operations—0
A generative foundation model for an all-in-one seismic processing framework—0
Structural Latency Perturbation in Large Language Models Through Recursive State Induction—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network—0
Converting Transformers into DGNNs FormCode0
Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified