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Found 6 papers, 0 papers with code
Semi-supervised Learning for Marked Temporal Point Processes
Despite the research focus, limited attention has been given to the challenging problem of developing solutions in semi-supervised settings, where algorithms have access to a mix of labeled and unlabeled data.
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles
We present an approach where two different models (Deep and Shallow) are trained separately on the data and a weighted average of the outputs is taken as the final result.
Crop Planning using Stochastic Visual Optimization
Given the weather and soil properties, farmers need to take critical decisions such as which seed variety to plant and in what proportion, in order to maximize productivity.
Comparative Benchmarking of Causal Discovery Techniques
In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual inference.
Deep Convolutional Neural Networks for Pairwise Causality
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning.
Warranty Cost Estimation Using Bayesian Network
All multi-component product manufacturing companies face the problem of warranty cost estimation.
