39 citations · 154 across the 26 of their papers we have counts for
19 papers · 1 filter
Topic Analysis for Text with Side Data
Biyi Fang, Kripa Rajshekhar, Diego Klabjan
Although latent factor models (e.g., matrix factorization) obtain good performance in predictions, they suffer from several problems including cold-start, non-transparency, and sub…
Tricks and Plugins to GBM on Images and Sequences
Biyi Fang, Jean Utke, Diego Klabjan
Convolutional neural networks (CNNs) and transformers, which are composed of multiple processing layers and blocks to learn the representations of data with multiple abstract level…
Open-Set Recognition of Breast Cancer Treatments
Alexander Cao, Diego Klabjan, Yuan Luo
Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with impro…
Aggregation Delayed Federated Learning
Ye Xue, Diego Klabjan, Yuan Luo
Federated learning is a distributed machine learning paradigm where multiple data owners (clients) collaboratively train one machine learning model while keeping data on their own…
Logit-based Uncertainty Measure in Classification
Huiyu Wu, Diego Klabjan
We introduce a new, reliable, and agnostic uncertainty measure for classification tasks called logit uncertainty. It is based on logit outputs of neural networks. We in particular…
A Probabilistic Approach to Neural Network Pruning
Xin Qian, Diego Klabjan
Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-pa…