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 3741–3750 of 4891 papers

TitleStatusHype
Self-Supervised Camera Self-Calibration from Video—0
Multi-scale Graph Convolutional Networks with Self-Attention—0
U2-Former: A Nested U-shaped Transformer for Image Restoration—0
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency—0
Few-Shot Data-Driven Algorithms for Low Rank Approximation—0
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity MeasurementCode0
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Hyperbolic Procrustes Analysis Using Riemannian GeometryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified