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 3126–3150 of 4891 papers

TitleStatusHype
Uncertainty Quantification in Portfolio Temperature Alignment—0
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods—0
Uncertainty Surrogates for Deep Learning—0
Unconditional Stability Analysis of N-Port Networks Based on Structured Singular Value Computation—0
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark—0
Understanding Neural Architecture Search Techniques—0
Understanding symmetries in deep networks—0
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks—0
Understanding the Variance Collapse of SVGD in High Dimensions—0
U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation—0
UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation—0
Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Universal-2-TF: Robust All-Neural Text Formatting for ASR—0
Universalizing Weak Supervision—0
Universal on-chip polarization handling with deep photonic networks—0
UniViTAR: Unified Vision Transformer with Native Resolution—0
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation—0
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training—0
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation—0
Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data—0
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement—0
Unsupervised Synchrony Discovery in Human Interaction—0
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations—0
UnWave-Net: Unrolled Wavelet Network for Compton Tomography Image Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified