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 2961–2970 of 4891 papers

TitleStatusHype
DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal ForecastingCode2
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Overcoming the Stability Gap in Continual Learning—0
Do we become wiser with time? On causal equivalence with tiered background knowledge—0
Interaction Measures, Partition Lattices and Kernel Tests for High-Order InteractionsCode0
TorchRL: A data-driven decision-making library for PyTorchCode4
Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesisCode4
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary DomainsCode1
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models—0
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria RecommendationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified