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 2081–2090 of 4891 papers

TitleStatusHype
Bayesian Quantum Neural Network for Renewable-Rich Power Flow with Training Efficiency and Generalization Capability Improvements—0
Bayesian Optimization for Hyperparameters Tuning in Neural Networks—0
Dual Conditional Diffusion Models for Sequential Recommendation—0
SkipSNN: Efficiently Classifying Spike Trains with Event-attentionCode0
AI-assisted Agile Propagation Modeling for Real-time Digital Twin Wireless Networks—0
Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Transformers—0
Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees—0
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning—0
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering—0
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte CarloCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified