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Found 10 papers, 2 papers with code
Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing
To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model.
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe.
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
We develop a new method -- SimCD -- that explicitly models cell heterogeneity and dynamic differential changes in one unified hierarchical gamma-negative binomial (hGNB) model, allowing simultaneous cell clustering and differential expression analysis for scRNA-seq data.
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition
In many machine learning tasks, input features with varying degrees of predictive capability are usually acquired at some cost.
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data.
Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.
Learnable Bernoulli Dropout for Bayesian Deep Learning
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters.
ARSM Gradient Estimator for Supervised Learning to Rank
Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches.
Optimal Clustering with Missing Values
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements.
