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 3661–3670 of 4891 papers

TitleStatusHype
Embedding Recurrent Layers with Dual-Path Strategy in a Variant of Convolutional Network for Speaker-Independent Speech Separation—0
Distributionally Robust Model Predictive Control with Total Variation Distance—0
DepthGAN: GAN-based Depth Generation of Indoor Scenes from Semantic Layouts—0
Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning—0
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC—0
Efficient universal shuffle attack for visual object tracking—0
Neural Theorem Provers Delineating Search Area Using RNN—0
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
Integrating Dependency Tree Into Self-attention for Sentence Representation—0
Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified