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 3641–3650 of 4891 papers

TitleStatusHype
Stochastic Primal-Dual Deep Unrolling—0
SIN:Superpixel Interpolation NetworkCode1
On Learning the Transformer KernelCode0
Counting Objects by Diffused Index: geometry-free and training-free approach—0
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies—0
Output Space Entropy Search Framework for Multi-Objective Bayesian OptimizationCode1
Benchmarking the Robustness of Spatial-Temporal Models Against CorruptionsCode1
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image ClassificationCode1
Action-Sufficient State Representation Learning for Control with Structural Constraints—0
Streaming on-device detection of device directed speech from voice and touch-based invocation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified