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 2921–2930 of 4891 papers

TitleStatusHype
Towards quantum enhanced adversarial robustness in machine learning—0
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoopCode1
Neural Multigrid Memory For Computational Fluid DynamicsCode0
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
InRank: Incremental Low-Rank LearningCode1
Generalized Random Forests using Fixed-Point TreesCode0
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems—0
HabiCrowd: A High Performance Simulator for Crowd-Aware Visual NavigationCode1
RoMe: Towards Large Scale Road Surface Reconstruction via Mesh RepresentationCode2
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified