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 3831–3840 of 4891 papers

TitleStatusHype
M-ar-K-Fast Independent Component AnalysisCode0
Seismic wave propagation and inversion with Neural Operators—0
Maximizing Influence with Graph Neural Networks—0
Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making—0
Value-Agnostic Conversational Semantic Parsing—0
Beyond Sentence-Level End-to-End Speech Translation: Context Helps—0
Dissecting FLOPs along input dimensions for GreenAI cost estimationsCode0
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding—0
A Frequency-based Parent Selection for Reducing the Effect of Evaluation Time Bias in Asynchronous Parallel Multi-objective Evolutionary Algorithms—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified