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 121–130 of 4891 papers

TitleStatusHype
Spatio-Temporal State Space Model For Efficient Event-Based Optical FlowCode1
OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting—0
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution—0
MapBERT: Bitwise Masked Modeling for Real-Time Semantic Mapping Generation—0
Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes—0
Accelerating Spectral Clustering under Fairness Constraints—0
Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning—0
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models—0
Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and PromptsCode0
Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified