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 3401–3425 of 4891 papers

TitleStatusHype
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network—0
Learning Positive Functions with Pseudo Mirror Descent—0
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression—0
Learning Representation for Multitask learning through Self Supervised Auxiliary learning—0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Learning Stochastic Parametric Differentiable Predictive Control Policies—0
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network—0
Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time—0
Learning the Update Operator for 2D/3D Image Registration—0
Learning to Control the Smoothness of Graph Convolutional Network Features—0
Learning to Defend by Learning to Attack—0
Learning-to-Defer for Extractive Question Answering—0
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks—0
Learning to Generate 3D Training Data Through Hybrid Gradient—0
Learning to Generate Content-Aware Dynamic Detectors—0
Learning to generate physical ocean states: Towards hybrid climate modeling—0
Learning to Generate Synthetic 3D Training Data through Hybrid Gradient—0
Maximizing Influence with Graph Neural Networks—0
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce—0
Learning to Optimize in Model Predictive Control—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Learning to sample in Cartesian MRI—0
Learning to Search Efficiently in High Dimensions—0
Learning to Search for Vehicle Routing with Multiple Time Windows—0
Learning to Speed Up Query Planning in Graph Databases—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified