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Found 15 papers, 2 papers with code
Learning Network Representations with Disentangled Graph Auto-Encoder
Learning disentangled graph representations with (variational) graph auto-encoder poses significant challenges, and remains largely unexplored in the existing literature.
Controlling the occurrence sequence of reaction modules through biochemical relaxation oscillators
We take the case of arbitrary multi-module regulation into consideration, analyze the main errors in the regulation process under \textit{mass-action kinetics} and demonstrate our design scheme under existing synthetic biochemical oscillator models.
Automatic Implementation of Neural Networks through Reaction Networks -- Part I: Circuit Design and Convergence Analysis
Information processing relying on biochemical interactions in the cellular environment is essential for biological organisms.
Causal Structure Learning by Using Intersection of Markov Blankets
In this paper, we introduce a novel causal structure learning algorithm called Endogenous and Exogenous Markov Blankets Intersection (EEMBI), which combines the properties of Bayesian networks and Structural Causal Models (SCM).
CauF-VAE: Causal Disentangled Representation Learning with VAE and Causal Flows
Disentangled representation learning aims to learn a low dimensional representation of data where each dimension corresponds to one underlying generative factor.
Domain Knowledge integrated for Blast Furnace Classifier Design
Blast furnace modeling and control is one of the important problems in the industrial field, and the black-box model is an effective mean to describe the complex blast furnace system.
Gaussian mixture modeling of nodes in Bayesian network according to maximal parental cliques
This paper uses Gaussian mixture model instead of linear Gaussian model to fit the distribution of every node in Bayesian network.
ELBD: Efficient score algorithm for feature selection on latent variables of VAE
In this paper, we develop the notion of evidence lower bound difference (ELBD), based on which an efficient score algorithm is presented to implement feature selection on latent variables of VAE and its variants.
Transfer Learning in Information Criteria-based Feature Selection
Our theoretical results indicate that, for any sample size in the target domain, the proposed TLCp estimator performs better than the Cp estimator by the mean squared error (MSE) metric in the case of orthogonal predictors, provided that i) the dissimilarity between the tasks from source domain and target domain is small, and ii) the procedure parameters (complexity penalties) are tuned according to certain explicit rules.
Online Interaction Detection for Click-Through Rate Prediction
Click-Through Rate prediction aims to predict the ratio of clicks to impressions of a specific link.
Discovering Categorical Main and Interaction Effects Based on Association Rule Mining
Association rule mining aims to extract interesting correlations between items, but it is difficult to use rules as a qualified classifier themselves.
Random Intersection Chains
The most confident patterns are finally returned by Random Intersection Chains.
Knowledge Integrated Classifier Design Based on Utility Optimization
This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge.
Efficacy of regularized multi-task learning based on SVM models
This paper investigates the efficacy of a regularized multi-task learning (MTL) framework based on SVM (M-SVM) to answer whether MTL always provides reliable results and how MTL outperforms independent learning.
Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors
The lack of interpretability often makes black-box models difficult to be applied to many practical domains.
