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 31–40 of 4891 papers

TitleStatusHype
Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesisCode4
TorchRL: A data-driven decision-making library for PyTorchCode4
Hierarchically Coherent Multivariate Mixture NetworksCode4
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph DataCode4
AudioLDM: Text-to-Audio Generation with Latent Diffusion ModelsCode4
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scaleCode3
NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal SimulationCode3
TensorNEAT: A GPU-accelerated Library for NeuroEvolution of Augmenting TopologiesCode3
GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-IIICode3
WeatherMesh-3: Fast and accurate operational global weather forecastingCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified