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 3111–3120 of 4891 papers

TitleStatusHype
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion ModelsCode0
Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach—0
Computation of safe disturbance sets using implicit RPI sets—0
LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel SegmentationCode0
Effective Real Image Editing with Accelerated Iterative Diffusion Inversion—0
HAct: Out-of-Distribution Detection with Neural Net Activation Histograms—0
Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network—0
CLEANing Cygnus A deep and fast with R2D2—0
Priority Queue Formulation of Agent-Based Bathtub Model for Network Trip Flows in the Relative Space—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified