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 4401–4410 of 4891 papers

TitleStatusHype
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection—0
Structured Bayesian Compression for Deep models in mobile enabled devices for connected healthcare—0
Optimization problems with low SWaP tactical Computing—0
MaCow: Masked Convolutional Generative FlowCode1
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing—0
Physics and Derivatives: Effective-Potential Path-Integral Approximations of Arrow-Debreu Densities—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling—0
Multi-Kernel Prediction Networks for Denoising of Burst ImagesCode0
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture DesignCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified