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 2571–2580 of 4891 papers

TitleStatusHype
Retrieval Augmentation via User Interest Clustering—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification—0
ReverBERT: A State Space Model for Efficient Text-Driven Speech Style Transfer—0
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Revisiting CHAMPAGNE: Sparse Bayesian Learning as Reweighted Sparse Coding—0
Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024—0
Revisiting Frank-Wolfe for Structured Nonconvex Optimization—0
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations—0
Revisiting Learning-based Video Motion Magnification for Real-time Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified