ArticleJournal of the American Statistical Association2022
Consistent Sparse Deep Learning: Theory and Computation.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- A weight-sharing Bayesian neural network for consistent feature selection with applications in cancer gene expression data.BMC bioinformatics · 2026Article
- Sparse-Input Neural Network using Group Concave Regularization.Transactions on machine learning research · 2025Article
- Mechanism and Prediction of Gray Jujube Fruit Quality Using Explainable ANN.Food science & nutrition · 2025Article
- A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules.Statistica Sinica · 2025Article
- Extended fiducial inference: toward an automated process of statistical inference.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- A survey of model compression techniques: past, present, and future.Frontiers in robotics and AI · 2025Review
- Extended fiducial inference for individual treatment effects via deep neural networks.Statistics and computing · 2025Article
- Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior.Journal of data science : JDS · 2024Article
- Deep network embedding with dimension selection.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Fast Value Tracking for Deep Reinforcement Learning.... International Conference on Learning Representations · 2024Article
- Causal-StoNet: Causal Inference for High-Dimensional Complex Data.... International Conference on Learning Representations · 2024Article
- A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.Statistics and its interface · 2024Article
- Learning Sparse Deep Neural Networks with a Spike-and-Slab Prior.Statistics & probability letters · 2022Article
- A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks.Statistics and computing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Deep learning has been the engine powering many successes of data science. However, the deep neural network (DNN), as the basic model of deep learning, is often excessively over-parameterized, causing many difficulties in training, prediction and interpretation. We propose a frequentist-like method for learning sparse DNNs and justify its consistency under the Bayesian framework: the proposed method could learn a sparse DNN with at most
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.