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 2501–2525 of 4891 papers

TitleStatusHype
Recurrent Relational Memory Network for Unsupervised Image Captioning—0
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming—0
Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization—0
Recursive Least Squares Advantage Actor-Critic Algorithms—0
Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model—0
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations—0
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints—0
Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research—0
Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation—0
Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences—0
RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and Detail Accuracy through Hierarchical Transformers and Progressive Refinement—0
RefreshNet: Learning Multiscale Dynamics through Hierarchical Refreshing—0
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation—0
Region Tracking in an Image Sequence: Preventing Driver Inattention—0
Regist3R: Incremental Registration with Stereo Foundation Model—0
ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction—0
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint—0
Regret Bounds for Robust Online Decision Making—0
The Nyström method for convex loss functions—0
Regularized ERM on random subspaces—0
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques—0
ReInc: Scaling Training of Dynamic Graph Neural Networks—0
Reinforcement Learning Based Symbolic Regression for Load Modeling—0
Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition—0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified