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

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Showing 2191–2200 of 4891 papers

TitleStatusHype
Spectral Densities, Structured Noise and Ensemble Averaging within Open Quantum Dynamics—0
Never Mind The No-Ops: Faster and Less Volatile Simulation Modelling of Co-Evolutionary Species Interactions via Spatial Cyclic GamesCode0
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions—0
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal TransportCode0
S7: Selective and Simplified State Space Layers for Sequence Modeling—0
A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical SystemsCode0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Remember and Recall: Associative-Memory-based Trajectory Prediction—0
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement—0
Efficient Second-Order Neural Network Optimization via Adaptive Trust Region Methods—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified