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 3701–3710 of 4891 papers

TitleStatusHype
Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach—0
Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory—0
Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation—0
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC—0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising—0
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features—0
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
Avoiding Obfuscation with Prover-Estimator Debate—0
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified