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 2161–2170 of 4891 papers

TitleStatusHype
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach—0
Self-Data Distillation for Recovering Quality in Pruned Large Language Models—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
Generalized Group Data Attribution—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position SearchCode0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation—0
Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications—0
Optimal Downsampling for Imbalanced Classification with Generalized Linear Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified