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 2311–2320 of 4891 papers

TitleStatusHype
Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning—0
TransDAE: Dual Attention Mechanism in a Hierarchical Transformer for Efficient Medical Image Segmentation—0
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained EnvironmentsCode0
Efficiently Expanding Receptive Fields: Local Split Attention and Parallel Aggregation for Enhanced Large-scale Point Cloud Semantic Segmentation—0
Robust Clustering on High-Dimensional Data with Stochastic QuantizationCode0
TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video—0
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation—0
Free-DyGS: Camera-Pose-Free Scene Reconstruction based on Gaussian Splatting for Dynamic Surgical Videos—0
Membership Inference Attacks Against In-Context Learning—0
Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified