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 3376–3400 of 4891 papers

TitleStatusHype
Learning Collective Behaviors from Observation—0
Learning Computational Efficient Bots with Costly Features—0
Learning Content-Aware Multi-Modal Joint Input Pruning via Bird's-Eye-View Representation—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Learning Deep Architectures via Generalized Whitened Neural Networks—0
Learning Depthwise Separable Graph Convolution from Data Manifold—0
Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction—0
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks—0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
Learning Flat Latent Manifolds with VAEs—0
Learning Interpretable Differentiable Logic Networks for Tabular Regression—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
Learning Item-Interaction Embeddings for User Recommendations—0
Learning k-Level Structured Sparse Neural Networks Using Group Envelope Regularization—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Learning linear structural equation models in polynomial time and sample complexity—0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences—0
Learning Navigation Subroutines from Egocentric Videos—0
Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration—0
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression—0
Learning Optimal Multimodal Information Bottleneck Representations—0
Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified