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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2041–2050 of 4891 papers

TitleStatusHype
Variance-Aware Linear UCB with Deep Representation for Neural Contextual BanditsCode0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning—0
Mitigating Stop-and-Go Traffic Congestion with Operator Learning—0
Multi-language Video Subtitle Dataset for Image-based Text Recognition—0
EffiCANet: Efficient Time Series Forecasting with Convolutional Attention—0
Benchmarking Large Language Models with Integer Sequence Generation Tasks—0
Discretized Gaussian Representation for Tomographic Reconstruction—0
Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation—0
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing RetrievalCode0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
Show:102550
← PrevPage 205 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified