1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
Classification Models for Partially Ordered Sequences
Stephanie Ger, Diego Klabjan, Jean Utke
Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units (GRUs) and transformers have been developed to classify time series data with the assumption that events i…
Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples
Jaehoon Koo, Diego Klabjan, Jean Utke
Most recent machine learning research focuses on developing new classifiers for the sake of improving classification accuracy. With many well-performing state-of-the-art classifier…
Unified recurrent neural network for many feature types
Alexander Stec, Diego Klabjan, Jean Utke
There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. asynchronous time series, provide a richer vari…
Combined convolutional and recurrent neural networks for hierarchical classification of images
Jaehoon Koo, Diego Klabjan, Jean Utke
Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a featur…
Nested multi-instance classification
Alexander Stec, Diego Klabjan, Jean Utke
There are classification tasks that take as inputs groups of images rather than single images. In order to address such situations, we introduce a nested multi-instance deep networ…