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 3041–3050 of 4891 papers

TitleStatusHype
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
Towards Assessing Deep Learning Test Input Generators—0
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World—0
Towards a Theory of Intentions for Human-Robot Collaboration—0
Towards a variational Jordan-Lee-Preskill quantum algorithm—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
Towards Compute-Optimal Transfer Learning—0
Towards Efficient Large Scale Spatial-Temporal Time Series Forecasting via Improved Inverted Transformers—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified