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 4101–4110 of 4891 papers

TitleStatusHype
Detection and Classification of Pole-like Landmarks for Domain-invariant 3D Point Cloud Map Matching—0
Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models—0
Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation—0
Development of modeling and control strategies for an approximated Gaussian process—0
DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions—0
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement—0
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization—0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—0
DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization—0
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified