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 2561–2570 of 4891 papers

TitleStatusHype
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing—0
Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems—0
RetGK: Graph Kernels based on Return Probabilities of Random Walks—0
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization—0
Rethinking Convolution: Towards an Optimal Efficiency—0
Rethinking Graph Transformer Architecture Design for Node Classification—0
Rethinking Numerical Representations for Deep Neural Networks—0
Rethinking Range-View LiDAR Segmentation in Adverse Weather—0
P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks—0
Rethinking Uncertainty Estimation in Natural Language Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified