39 citations · 154 across the 26 of their papers we have counts for
10 papers · 1 filter
Layer Flexible Adaptive Computational Time
Lida Zhang, Abdolghani Ebrahimi, Diego Klabjan
Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of…
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…
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks
Mark Harmon, Diego Klabjan
Recurrent neural networks and sequence to sequence models require a predetermined length for prediction output length. Our model addresses this by allowing the network to predict a…
Forecasting Crime with Deep Learning
Alexander Stec, Diego Klabjan
The objective of this work is to take advantage of deep neural networks in order to make next day crime count predictions in a fine-grain city partition. We make predictions using…