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 1461–1470 of 4891 papers

TitleStatusHype
Benchmarking Large Language Models with Integer Sequence Generation Tasks—0
Discretized Gaussian Representation for Tomographic Reconstruction—0
Multi-language Video Subtitle Dataset for Image-based Text Recognition—0
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing RetrievalCode0
Robust and Fast Bass local volatility—0
Reconsidering the Performance of GAE in Link PredictionCode1
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Energy-based physics-informed neural network for frictionless contact problems under large deformationCode1
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified