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 1801–1810 of 4891 papers

TitleStatusHype
Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback—0
Bottom-up robust modeling for the foraging behavior of Physarum polycephalum—0
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement—0
Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers—0
Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models—0
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks—0
HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout AnalysisCode0
Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable Multivariate Time Series Forecasting—0
Exact Acceleration of Subgraph Graph Neural Networks by Eliminating Computation Redundancy—0
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified