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 3601–3610 of 4891 papers

TitleStatusHype
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Few-Shot Data-Driven Algorithms for Low Rank Approximation—0
Sparse DETR: Efficient End-to-End Object Detection with Learnable SparsityCode1
Characteristic Neural Ordinary Differential Equations—0
Adaptive Fourier Neural Operators: Efficient Token Mixers for TransformersCode1
SpeechMoE2: Mixture-of-Experts Model with Improved Routing—0
Cycle Consistent Probability Divergences Across Different Spaces—0
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length—0
Using Convolutional Neural Networks to Detect Compression Algorithms—0
MOROCCO: Model Resource Comparison FrameworkCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified