# Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data

arXiv: https://arxiv.org/abs/1709.08073 · HTML: https://mlsanity.com/paper/1709.08073/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Differentiable Network Pruning for Microcontrollers](https://mlsanity.com/paper/2110.08350/) (2021)
- [Turning old models fashion again: Recycling classical CNN networks using the Lattice Transformation](https://mlsanity.com/paper/2109.13885/) (2021)

## Abstract (arXiv)

We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters. Finally, we visualise the model's predictions, revealing implications about latent variables in this task.