Log-Nonlinear Formulations for Robust High-dimensional Modeling

2018 ◽  
Keyword(s):  
High Dimensional ◽  
Dimensional Modeling

scholarly journals Performance-optimized neural networks as an explanatory framework for decision confidence

2021 ◽  
Author(s):  
Taylor W Webb ◽  
Kiyofumi Miyoshi ◽  
Tsz Yan So ◽  
Sivananda Rajananda ◽  
Hakwan Lau
Keyword(s):  
Neural Network ◽  
Signal Detection Theory ◽  
Deep Neural Network ◽  
High Dimensional ◽  
Decision Variable ◽  
Sensory Inputs ◽  
Decision Confidence ◽  
Dimensional Modeling ◽  
Principled Explanation ◽  
Low Dimensional

Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional modeling frameworks, such as signal detection theory or Bayesian inference, leaving open the question of how decision confidence operates in the domain of high-dimensional, naturalistic stimuli. To address this, we developed a deep neural network model optimized to assess decision confidence directly given high-dimensional inputs such as images. The model naturally accounts for a number of puzzling dissociations between decisions and confidence, suggests a principled explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

scholarly journals Consistent Group Selection with Bayesian High Dimensional Modeling

2020 ◽  
Vol 15 (3) ◽  
pp. 909-935 ◽  
Author(s):  
Xinming Yang ◽  
Naveen N. Narisetty
Keyword(s):  
Group Selection ◽  
High Dimensional ◽  
Dimensional Modeling ◽  
Consistent Group

scholarly journals Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates

2017 ◽  
Vol 43 (1) ◽  
pp. 3-31 ◽  
Author(s):  
Adam C. Sales ◽  
Ben B. Hansen ◽  
Brian Rowan
Keyword(s):  
School Level ◽  
High Dimensional ◽  
Confounding Bias ◽  
Control Subjects ◽  
Dimensional Modeling ◽  
Reform Program ◽  
Machine Learning Model ◽  
Matching Estimator ◽  
Theoretical Results ◽  
Reducing Properties

In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces “rebar,” a method using high-dimensional modeling to incorporate these commonly discarded data without sacrificing the integrity of the matching design. After constructing a match, a researcher uses the unmatched control subjects—the remnant—to fit a machine learning model predicting control potential outcomes as a function of the full covariate matrix. The resulting predictions in the matched set are used to adjust the causal estimate to reduce confounding bias. We present theoretical results to justify the method’s bias-reducing properties as well as a simulation study that demonstrates them. Additionally, we illustrate the method in an evaluation of a school-level comprehensive educational reform program in Arizona.

A MEMORY-BASED SELF-GENERATED BASIS FUNCTION NEURAL NETWORK

1999 ◽  
Vol 09 (01) ◽  
pp. 41-59 ◽  
Author(s):  
CHUN-SHIN LIN ◽  
CHIEN-KUO LI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Basis Function ◽  
Convergence Property ◽  
High Dimensional ◽  
Memory Space ◽  
Dimensional Modeling ◽  
The Neural Network ◽  
Multilayer Neural Networks ◽  
Learning Convergence

The paper presents a novel memory-based Self-Generated Basis Function Neural Network (SGBFN) that is composed of small CMACs. The SGBFN requires much smaller memory space than the conventional CMAC and has an excellent learning convergence property compared to multilayer neural networks. Each CMAC in the new structure takes a subset of problem inputs as its inputs. Several CMACs that have different subsets of inputs form a submodule and a group of submodules form a neural network. The output of a submodule is the product of its CMACs' outputs. Each submodule implements a self-generated basis function, which is developed during the learning. The output of the neural network is the sum of the outputs from the submodules. Using only a subset of inputs in each CMAC significantly reduces the required memory space in high-dimensional modeling. With the same size of memory, the new structure is able to achieve a much smaller learning error compared to the conventional CMAC.

scholarly journals Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques

2015 ◽  
Vol 51 (1) ◽  
pp. 220-239 ◽  
Author(s):  
Jessica M. Franklin ◽  
William H. Shrank ◽  
Joyce Lii ◽  
Alexis K. Krumme ◽  
Olga S. Matlin ◽  
...  
Keyword(s):  
High Dimensional ◽  
Dimensional Modeling ◽  
Modeling Techniques
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