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 3871–3880 of 4891 papers

TitleStatusHype
End-to-end reconstruction meets data-driven regularization for inverse problemsCode0
Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximationsCode0
Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography—0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Nonuniform Defocus Removal for Image Classification—0
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse KinematicsCode0
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression—0
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue SystemsCode0
Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence—0
Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified