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 3001–3010 of 4891 papers

TitleStatusHype
Riemannian Laplace Approximation with the Fisher MetricCode0
Successor Features for Efficient Multisubject Controlled Text Generation—0
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine—0
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders—0
Electronic excited states from physically-constrained machine learning—0
Learning Collective Behaviors from Observation—0
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes—0
Bandit-Driven Batch Selection for Robust Learning under Label Noise—0
Interpretable Neural PDE Solvers using Symbolic Frameworks—0
Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified