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 941–950 of 4891 papers

TitleStatusHype
Flopping for FLOPs: Leveraging equivariance for computational efficiency—0
Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures—0
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient—0
Tighter sparse variational Gaussian processes—0
Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation—0
OneTrack-M: A multitask approach to transformer-based MOT models—0
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers—0
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified