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 2791–2800 of 4891 papers

TitleStatusHype
Large Language Models Are Not Robust Multiple Choice SelectorsCode1
Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network—0
Simulating room transfer functions between transducers mounted on audio devices using a modified image source methodCode1
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
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography—0
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth EstimationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified