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 1041–1050 of 4891 papers

TitleStatusHype
Pseudocode-Injection Magic: Enabling LLMs to Tackle Graph Computational Tasks—0
Radio Map Estimation via Latent Domain Plug-and-Play DenoisingCode0
PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics—0
Architectural Fusion Through Contextual Partitioning in Large Language Models: A Novel Approach to Parameterized Knowledge Integration—0
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning—0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
Heuristic Deep Reinforcement Learning for Phase Shift Optimization in RIS-assisted Secure Satellite Communication Systems with RSMA—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified