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 1391–1400 of 4891 papers

TitleStatusHype
Recursive Gaussian Process State Space ModelCode1
Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data ProcessingCode3
Exploring Kolmogorov-Arnold Networks for Interpretable Time Series ClassificationCode0
A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its BenefitsCode0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
Implicit and Parametric Avatar Pose and Shape Estimation From a Single Frontal Image of a Clothed HumanCode0
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net—0
Fast Stochastic MPC using Affine Disturbance Feedback Gains Learned Offline—0
Analytical Formula for Fractional-Order Conditional Moments of Nonlinear Drift CEV Process with Regime Switching: Hybrid Approach with Applications—0
Variational Autoencoders for Efficient Simulation-Based Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified