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 2091–2100 of 4891 papers

TitleStatusHype
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte CarloCode0
A Temporal Linear Network for Time Series ForecastingCode0
Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns—0
Optimal Hardening Strategy for Electricity-Hydrogen Networks with Hydrogen Leakage Risk Control against Extreme Weather—0
Hybrid Deep Learning for Legal Text Analysis: Predicting Punishment Durations in Indonesian Court Rulings—0
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data AssimilationCode0
Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search—0
Prompt Diffusion Robustifies Any-Modality Prompt Learning—0
GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction—0
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified