# Gated recurrent units and temporal convolutional network for multilabel classification

arXiv: https://arxiv.org/abs/2110.04414 · HTML: https://mlsanity.com/paper/2110.04414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 73 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Light Gated Recurrent Units for Speech Recognition](https://mlsanity.com/paper/1803.10225/) (2018)
- [Orderless Recurrent Models for Multi-label Classification](https://mlsanity.com/paper/1911.09996/) (2019)
- [Gated Orthogonal Recurrent Units: On Learning to Forget](https://mlsanity.com/paper/1706.02761/) (2017)
- [diffGrad: An Optimization Method for Convolutional Neural Networks](https://mlsanity.com/paper/1909.11015/) (2019)
- [Multi-Label Learning with Deep Forest](https://mlsanity.com/paper/1911.06557/) (2019)
- [On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network](https://mlsanity.com/paper/1803.01686/) (2018)
- [Deep tree-ensembles for multi-output prediction](https://mlsanity.com/paper/2011.02829/) (2020)
- [Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks](https://mlsanity.com/paper/2103.14689/) (2021)
- [High performing ensemble of convolutional neural networks for insect pest image detection](https://mlsanity.com/paper/2108.12539/) (2021)
- [LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1905.08983/) (2019)
- [Incorporating Multiple Cluster Centers for Multi-Label Learning](https://mlsanity.com/paper/2004.08113/) (2020)
- [Neural networks for Anatomical Therapeutic Chemical (ATC) classification](https://mlsanity.com/paper/2101.11713/) (2021)

## Abstract (arXiv)

Multilabel learning tackles the problem of associating a sample with multiple class labels. This work proposes a new ensemble method for managing multilabel classification: the core of the proposed approach combines a set of gated recurrent units and temporal convolutional neural networks trained with variants of the Adam optimization approach. Multiple Adam variants, including novel one proposed here, are compared and tested; these variants are based on the difference between present and past gradients, with step size adjusted for each parameter. The proposed neural network approach is also combined with Incorporating Multiple Clustering Centers (IMCC), which further boosts classification performance. Multiple experiments on nine data sets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art. The MATLAB code for generating the best ensembles in the experimental section will be available at https://github.com/LorisNanni.