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 2511–2520 of 4891 papers

TitleStatusHype
Uncertainty quantification for probabilistic machine learning in earth observation using conformal predictionCode1
A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting—0
TRIPS: Trilinear Point Splatting for Real-Time Radiance Field RenderingCode4
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spacesCode1
Feature Network Methods in Machine Learning and Applications—0
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks—0
DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
SeTformer is What You Need for Vision and Language—0
Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified