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 4331–4340 of 4891 papers

TitleStatusHype
Multi-level Wavelet Convolutional Neural NetworksCode0
Learning to Generate Synthetic 3D Training Data through Hybrid Gradient—0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
Compositionally-Warped Gaussian Processes—0
Trade-offs in Large-Scale Distributed Tuplewise Estimation and LearningCode0
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization—0
Object Placement on Cluttered Surfaces: A Nested Local Search Approach—0
Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation—0
Differentiable probabilistic models of scientific imaging with the Fourier slice theoremCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified