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 1511–1520 of 4891 papers

TitleStatusHype
Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees—0
Quantum Reinforcement Learning-Based Two-Stage Unit Commitment Framework for Enhanced Power Systems Robustness—0
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte CarloCode0
A Temporal Linear Network for Time Series ForecastingCode0
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?Code1
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent CodingCode1
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning—0
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering—0
Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning ModelsCode2
Optimal Hardening Strategy for Electricity-Hydrogen Networks with Hydrogen Leakage Risk Control against Extreme Weather—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified