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 1341–1350 of 4891 papers

TitleStatusHype
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
Dynamic Token Selection for Aerial-Ground Person Re-Identification—0
Nonlinearity and Uncertainty Informed Moment-Matching Gaussian Mixture SplittingCode0
QuAKE: Speeding up Model Inference Using Quick and Approximate Kernels for Exponential Non-Linearities—0
Distributed Differentially Private Data Analytics via Secure Sketching—0
HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks—0
Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning—0
Wafer2Spike: Spiking Neural Network for Wafer Map Pattern ClassificationCode0
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations—0
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified