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 1451–1460 of 4891 papers

TitleStatusHype
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential RecommendationCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
DBgDel: Database-Enhanced Gene Deletion Framework for Growth-Coupled Production in Genome-Scale Metabolic ModelsCode0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
E-detectors: a nonparametric framework for sequential change detectionCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified