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 3876–3900 of 4891 papers

TitleStatusHype
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning—0
The Variational Bayesian Inference for Network Autoregression Models—0
RFI Mitigation for One-bit UWB Radar Systems—0
On the Fundamental Limits of Exact Inference in Structured Prediction—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks—0
Learning a Product Relevance Model from Click-Through Data in E-Commerce—0
Tight lower bounds for Dynamic Time WarpingCode0
Cerebral cortical communication overshadows computational energy-use, but these combine to predict synapse number—0
Dynamic Neural Networks: A Survey—0
Fast and Accurate Amplitude Demodulation of Wideband SignalsCode0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO—0
A Universal Framework for Featurization of Atomistic SystemsCode0
An efficient optimization based microstructure reconstruction approach with multiple loss functions—0
Learning the Update Operator for 2D/3D Image Registration—0
Subdimensional Expansion for Multi-objective Multi-agent Path FindingCode1
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation—0
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark—0
A Multiscale Environment for Learning by DiffusionCode0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Recurrent Localization Networks applied to the Lippmann-Schwinger EquationCode0
Decision Machines: Congruent Decision Trees—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified