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 1971–1980 of 4891 papers

TitleStatusHype
DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing—0
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression—0
UVCG: Leveraging Temporal Consistency for Universal Video Protection—0
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models—0
LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration—0
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting—0
Broad Critic Deep Actor Reinforcement Learning for Continuous Control—0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Efficient Ternary Weight Embedding Model: Bridging Scalability and PerformanceCode0
Multi-scale Cascaded Large-Model for Whole-body ROI SegmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified