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 1761–1770 of 4891 papers

TitleStatusHype
Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural NetworksCode1
Cross-attention Inspired Selective State Space Models for Target Sound ExtractionCode1
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis—0
On the Complexity of Neural Computation in Superposition—0
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT ModelsCode0
Parallel AutoRegressive Models for Multi-Agent Combinatorial OptimizationCode1
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified