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 3851–3860 of 4891 papers

TitleStatusHype
Joint Network Topology Inference via Structured Fusion Regularization—0
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference—0
Real-World Single Image Super-Resolution: A Brief ReviewCode1
Anharmonic Raman spectra simulation of crystals from deep neural networks—0
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place RecognitionCode1
A Pose-only Solution to Visual Reconstruction and NavigationCode1
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech SeparationCode1
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified